fix tf bc default test

.circleci/config.yml

             . venv/bin/activate
             mkdir test-reports
             pip freeze > test-reports/pip_versions.txt
-            pytest -n 2 --cov=ml-agents --cov=ml-agents-envs --cov=gym-unity --cov-report html --junitxml=test-reports/junit.xml -p no:warnings
+            pytest --cov=ml-agents --cov=ml-agents-envs --cov=gym-unity --cov-report html --junitxml=test-reports/junit.xml -p no:warnings
 
       - run:
           name: Verify there are no hidden/missing metafiles.

test_requirements.txt

 pytest-cov==2.6.1
 pytest-xdist
 
+# PyTorch tests are here for the time being, before they are used in the codebase.
+torch>=1.5.0
+
 # onnx doesn't currently have a wheel for 3.8
 tf2onnx>=1.5.5;python_version<'3.8'

Project/ProjectSettings/EditorBuildSettings.asset

 EditorBuildSettings:
   m_ObjectHideFlags: 0
   serializedVersion: 2
-  m_Scenes: []
+  m_Scenes:
+  - enabled: 1
+    path: Assets/ML-Agents/Examples/3DBall/Scenes/3DBall.unity
+    guid: b9ac0cbf961bf4dacbfa0aa9c0d60aaa
   m_configObjects: {}

Project/ProjectSettings/UnityConnectSettings.asset

 UnityConnectSettings:
   m_ObjectHideFlags: 0
   serializedVersion: 1
-  m_Enabled: 1
+  m_Enabled: 0
   m_TestMode: 0
   m_EventOldUrl: https://api.uca.cloud.unity3d.com/v1/events
   m_EventUrl: https://cdp.cloud.unity3d.com/v1/events

docs/Training-ML-Agents.md

 ended.
 
 
+#### Curriculum
+
+To enable curriculum learning, you need to add a `curriculum` sub-section to your environment
+parameter. Here is one example with the environment parameter `my_environment_parameter` :
+
+```yml
+behaviors:
+  BehaviorY:
+    # < Same as above >
+
+# Add this section
+environment_parameters:
+  my_environment_parameter:
+    curriculum:
+      - name: MyFirstLesson # The '-' is important as this is a list
+        completion_criteria:
+          measure: progress
+          behavior: my_behavior
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.2
+        value: 0.0
+      - name: MySecondLesson # This is the start of the second lesson
+        completion_criteria:
+          measure: progress
+          behavior: my_behavior
+          signal_smoothing: true
+          min_lesson_length: 100
+          threshold: 0.6
+          require_reset: true
+        value:
+          sampler_type: uniform
+          sampler_parameters:
+            min_value: 4.0
+            max_value: 7.0
+      - name: MyLastLesson
+        value: 8.0
+```
+
+Note that this curriculum __only__ applies to `my_environment_parameter`. The `curriculum` section
+contains a list of `Lessons`. In the example, the lessons are named `MyFirstLesson`, `MySecondLesson`
+and `MyLastLesson`.
+Each `Lesson` has 3 fields :
+
+ - `name` which is a user defined name for the lesson (The name of the lesson will be displayed in
+ the console when the lesson changes)
+ - `completion_criteria` which determines what needs to happen in the simulation before the lesson
+ can be considered complete. When that condition is met, the curriculum moves on to the next
+ `Lesson`. Note that you do not need to specify a `completion_criteria` for the last `Lesson`
+ - `value` which is the value the environment parameter will take during the lesson. Note that this
+ can be a float or a sampler.
+
+ There are the different settings of the `completion_criteria` :
+
+
+| **Setting**         | **Description**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| :------------------ | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `measure`           | What to measure learning progress, and advancement in lessons by.<br><br> `reward` uses a measure received reward, while `progress` uses the ratio of steps/max_steps.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+| `behavior`        | Specifies which behavior is being tracked. There can be multiple behaviors with different names, each at different points of training. This setting allows the curriculum to track only one of them.                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| `threshold`        | Determines at what point in value of `measure` the lesson should be increased.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| `min_lesson_length` | The minimum number of episodes that should be completed before the lesson can change. If `measure` is set to `reward`, the average cumulative reward of the last `min_lesson_length` episodes will be used to determine if the lesson should change. Must be nonnegative. <br><br> **Important**: the average reward that is compared to the thresholds is different than the mean reward that is logged to the console. For example, if `min_lesson_length` is `100`, the lesson will increment after the average cumulative reward of the last `100` episodes exceeds the current threshold. The mean reward logged to the console is dictated by the `summary_freq` parameter defined above. |
+| `signal_smoothing`  | Whether to weight the current progress measure by previous values.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| `require_reset`  | Whether changing lesson requires the environment to reset (default: false)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+##### Training with a Curriculum
+
+Once we have specified our metacurriculum and curricula, we can launch
+`mlagents-learn` to point to the config file containing
+our curricula and PPO will train using Curriculum Learning. For example, to
+train agents in the Wall Jump environment with curriculum learning, we can run:
+
+```sh
+mlagents-learn config/ppo/WallJump_curriculum.yaml --run-id=wall-jump-curriculum
+```
+
+We can then keep track of the current lessons and progresses via TensorBoard. If you've terminated
+the run, you can resume it using `--resume` and lesson progress will start off where it
+ended.
+
+
 ### Training Using Concurrent Unity Instances
 
 In order to run concurrent Unity instances during training, set the number of

ml-agents-envs/mlagents_envs/communicator_objects/unity_to_external_pb2_grpc.py

 from mlagents_envs.communicator_objects import unity_message_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__message__pb2
 
 
-class UnityToExternalProtoStub(object):
+class UnityToExternalProtoStub:
   # missing associated documentation comment in .proto file
   pass
 
         )
 
 
-class UnityToExternalProtoServicer(object):
+class UnityToExternalProtoServicer:
   # missing associated documentation comment in .proto file
   pass
 

ml-agents/mlagents/trainers/buffer.py

             Adds a list of np.arrays to the end of the list of np.arrays.
             :param data: The np.array list to append.
             """
-            self += list(np.array(data))
+            self += list(np.array(data, dtype=np.float32))
 
         def set(self, data):
             """

ml-agents/mlagents/trainers/cli_utils.py

         action=DetectDefaultStoreTrue,
         help="Forces training using CPU only",
     )
+    argparser.add_argument(
+        "--torch",
+        default=False,
+        action=DetectDefaultStoreTrue,
+        help="(Experimental) Use the PyTorch framework instead of TensorFlow. Install PyTorch "
+        "before using this option",
+    )
 
     eng_conf = argparser.add_argument_group(title="Engine Configuration")
     eng_conf.add_argument(

ml-agents/mlagents/trainers/learn.py

 )
 from mlagents.trainers.cli_utils import parser
 from mlagents_envs.environment import UnityEnvironment
-from mlagents.trainers.settings import RunOptions
+from mlagents.trainers.settings import RunOptions, TestingConfiguration
 
 from mlagents.trainers.training_status import GlobalTrainingStatus
 from mlagents_envs.base_env import BaseEnv
     add_metadata as add_timer_metadata,
 )
 from mlagents_envs import logging_util
+
+from mlagents_envs.registry import default_registry
 
 logger = logging_util.get_logger(__name__)
 
     ) -> UnityEnvironment:
         # Make sure that each environment gets a different seed
         env_seed = seed + worker_id
-        return UnityEnvironment(
-            file_name=env_path,
-            worker_id=worker_id,
-            seed=env_seed,
-            no_graphics=no_graphics,
-            base_port=start_port,
-            additional_args=env_args,
-            side_channels=side_channels,
-            log_folder=log_folder,
-        )
+        if TestingConfiguration.env_name == "":
+            return UnityEnvironment(
+                file_name=env_path,
+                worker_id=worker_id,
+                seed=env_seed,
+                no_graphics=no_graphics,
+                base_port=start_port,
+                additional_args=env_args,
+                side_channels=side_channels,
+                log_folder=log_folder,
+            )
+        else:
+            return default_registry[TestingConfiguration.env_name].make(
+                seed=env_seed,
+                no_graphics=no_graphics,
+                base_port=start_port,
+                worker_id=worker_id,
+                additional_args=env_args,
+                side_channels=side_channels,
+                log_folder=log_folder,
+            )
 
     return create_unity_environment
 

ml-agents/mlagents/trainers/settings.py

     return {key: cattr.unstructure(val) for key, val in d.items()}
 
 
+class TestingConfiguration:
+    use_torch = True
+    max_steps = 0
+    env_name = ""
+    device = "cpu"
+
+
 @attr.s(auto_attribs=True)
 class ExportableSettings:
     def as_dict(self):
         return _mapping[self]
 
 
+class FrameworkType(Enum):
+    TENSORFLOW: str = "tensorflow"
+    PYTORCH: str = "pytorch"
+
+
 @attr.s(auto_attribs=True)
 class TrainerSettings(ExportableSettings):
     trainer_type: TrainerType = TrainerType.PPO
     threaded: bool = True
     self_play: Optional[SelfPlaySettings] = None
     behavioral_cloning: Optional[BehavioralCloningSettings] = None
+    framework: FrameworkType = FrameworkType.TENSORFLOW
 
     cattr.register_structure_hook(
         Dict[RewardSignalType, RewardSignalSettings], RewardSignalSettings.structure
                     configured_dict["engine_settings"][key] = val
                 else:  # Base options
                     configured_dict[key] = val
-        return RunOptions.from_dict(configured_dict)
+
+        # Apply --torch retroactively
+        final_runoptions = RunOptions.from_dict(configured_dict)
+        if "torch" in DetectDefault.non_default_args:
+            for trainer_set in final_runoptions.behaviors.values():
+                trainer_set.framework = FrameworkType.PYTORCH
+        return final_runoptions
 
     @staticmethod
     def from_dict(options_dict: Dict[str, Any]) -> "RunOptions":

ml-agents/mlagents/trainers/trainer/rl_trainer.py

 from mlagents.trainers.optimizer import Optimizer
 from mlagents.trainers.buffer import AgentBuffer
 from mlagents.trainers.trainer import Trainer
-from mlagents.trainers.components.reward_signals import RewardSignalResult
+from mlagents.trainers.components.reward_signals import RewardSignalResult, RewardSignal
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.policy.policy import Policy
+from mlagents.trainers.policy.tf_policy import TFPolicy
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.settings import TestingConfiguration, FrameworkType
+from mlagents.trainers.exception import UnityTrainerException
+
+try:
+    from mlagents.trainers.policy.torch_policy import TorchPolicy
+except ModuleNotFoundError:
+    TorchPolicy = None  # type: ignore
 
 RewardSignalResults = Dict[str, RewardSignalResult]
 
         self._stats_reporter.add_property(
             StatsPropertyType.HYPERPARAMETERS, self.trainer_settings.as_dict()
         )
+        self.framework = self.trainer_settings.framework
+        logger.debug(f"Using framework {self.framework.value}")
+
+        if TestingConfiguration.max_steps > 0:
+            self.trainer_settings.max_steps = TestingConfiguration.max_steps
         self._next_save_step = 0
         self._next_summary_step = 0
 
                 self.reward_buffer.appendleft(rewards.get(agent_id, 0))
                 rewards[agent_id] = 0
             else:
-                self.stats_reporter.add_stat(
-                    optimizer.reward_signals[name].stat_name, rewards.get(agent_id, 0)
-                )
+                if isinstance(optimizer.reward_signals[name], RewardSignal):
+                    self.stats_reporter.add_stat(
+                        optimizer.reward_signals[name].stat_name,
+                        rewards.get(agent_id, 0),
+                    )
+                else:
+                    self.stats_reporter.add_stat(
+                        f"Policy/{optimizer.reward_signals[name].name.capitalize()} Reward",
+                        rewards.get(agent_id, 0),
+                    )
                 rewards[agent_id] = 0
 
     def _clear_update_buffer(self) -> None:
         """
         return False
 
+    def create_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> Policy:
+        if self.framework == FrameworkType.PYTORCH and TorchPolicy is None:
+            raise UnityTrainerException(
+                "To use the experimental PyTorch backend, install the PyTorch Python package first."
+            )
+        elif self.framework == FrameworkType.PYTORCH:
+            return self.create_torch_policy(parsed_behavior_id, behavior_spec)
+        else:
+            return self.create_tf_policy(parsed_behavior_id, behavior_spec)
+
+    @abc.abstractmethod
+    def create_torch_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TorchPolicy:
+        """
+        Create a Policy object that uses the PyTorch backend.
+        """
+        pass
+
+    @abc.abstractmethod
+    def create_tf_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TFPolicy:
+        """
+        Create a Policy object that uses the TensorFlow backend.
+        """
+        pass
+
     def _policy_mean_reward(self) -> Optional[float]:
         """ Returns the mean episode reward for the current policy. """
         rewards = self.cumulative_returns_since_policy_update
             logger.warning(
                 "Trainer has multiple policies, but default behavior only saves the first."
             )
+        elif n_policies == 0:
+            logger.warning("Trainer has no policies, not saving anything.")
+            return
         policy = list(self.policies.values())[0]
         model_checkpoint = self._checkpoint()
 

ml-agents/mlagents/trainers/ppo/optimizer_tf.py

             name="old_probabilities",
         )
 
-        # Break old log probs into separate branches
+        # Break old log log_probs into separate branches
         old_log_prob_branches = ModelUtils.break_into_branches(
             self.all_old_log_probs, self.policy.act_size
         )

ml-agents/mlagents/trainers/ppo/trainer.py

 from mlagents.trainers.trainer.rl_trainer import RLTrainer
 from mlagents.trainers.policy import Policy
 from mlagents.trainers.policy.tf_policy import TFPolicy
-from mlagents.trainers.ppo.optimizer import PPOOptimizer
+from mlagents.trainers.ppo.optimizer_tf import PPOOptimizer
-from mlagents.trainers.settings import TrainerSettings, PPOSettings
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    PPOSettings,
+    TestingConfiguration,
+    FrameworkType,
+)
+from mlagents.trainers.components.reward_signals import RewardSignal
+
+try:
+    from mlagents.trainers.policy.torch_policy import TorchPolicy
+    from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
+except ModuleNotFoundError:
+    TorchPolicy = None  # type: ignore
+    TorchPPOOptimizer = None  # type: ignore
 
 
 logger = get_logger(__name__)
         )
         self.load = load
         self.seed = seed
+        if TestingConfiguration.max_steps > 0:
+            self.trainer_settings.max_steps = TestingConfiguration.max_steps
         self.policy: Policy = None  # type: ignore
 
     def _process_trajectory(self, trajectory: Trajectory) -> None:
             trajectory.next_obs,
             trajectory.done_reached and not trajectory.interrupted,
         )
+
-            self._stats_reporter.add_stat(
-                self.optimizer.reward_signals[name].value_name, np.mean(v)
-            )
+            if isinstance(self.optimizer.reward_signals[name], RewardSignal):
+                self._stats_reporter.add_stat(
+                    self.optimizer.reward_signals[name].value_name, np.mean(v)
+                )
+            else:
+                self._stats_reporter.add_stat(
+                    f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate",
+                    np.mean(v),
+                )
 
         # Evaluate all reward functions
         self.collected_rewards["environment"][agent_id] += np.sum(
-            evaluate_result = reward_signal.evaluate_batch(
-                agent_buffer_trajectory
-            ).scaled_reward
+            if isinstance(reward_signal, RewardSignal):
+                evaluate_result = reward_signal.evaluate_batch(
+                    agent_buffer_trajectory
+                ).scaled_reward
+            else:
+                evaluate_result = (
+                    reward_signal.evaluate(agent_buffer_trajectory)
+                    * reward_signal.strength
+                )
             agent_buffer_trajectory[f"{name}_rewards"].extend(evaluate_result)
             # Report the reward signals
             self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
             local_value_estimates = agent_buffer_trajectory[
                 f"{name}_value_estimates"
             ].get_batch()
+
             local_advantage = get_gae(
                 rewards=local_rewards,
                 value_estimates=local_value_estimates,
         self._clear_update_buffer()
         return True
 
-    def create_policy(
+    def create_tf_policy(
         self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
     ) -> TFPolicy:
         """
             model_path=self.artifact_path,
             load=self.load,
             condition_sigma_on_obs=False,  # Faster training for PPO
-            create_tf_graph=False,  # We will create the TF graph in the Optimizer
+        return policy
+    def create_torch_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TorchPolicy:
+        """
+        Creates a PPO policy to trainers list of policies.
+        :param parsed_behavior_id:
+        :param brain_parameters: specifications for policy construction
+        :return policy
+        """
+        policy = TorchPolicy(
+            self.seed,
+            behavior_spec,
+            self.trainer_settings,
+            self.artifact_path,
+            self.load,
+            condition_sigma_on_obs=False,  # Faster training for PPO
+            separate_critic=behavior_spec.is_action_continuous(),
+        )
         return policy
 
     def add_policy(
             )
         self.policy = policy
         self.policies[parsed_behavior_id.behavior_id] = policy
-        self.optimizer = PPOOptimizer(
-            cast(TFPolicy, self.policy), self.trainer_settings
-        )
+        if self.framework == FrameworkType.PYTORCH:
+            self.optimizer = TorchPPOOptimizer(  # type: ignore
+                self.policy, self.trainer_settings  # type: ignore
+            )  # type: ignore
+        else:
+            self.optimizer = PPOOptimizer(  # type: ignore
+                self.policy, self.trainer_settings  # type: ignore
+            )  # type: ignore
         for _reward_signal in self.optimizer.reward_signals.keys():
             self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
         # Needed to resume loads properly

ml-agents/mlagents/trainers/sac/trainer.py

 from mlagents.trainers.trainer.rl_trainer import RLTrainer
 from mlagents.trainers.trajectory import Trajectory, SplitObservations
 from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
-from mlagents.trainers.settings import TrainerSettings, SACSettings
+from mlagents.trainers.settings import TrainerSettings, SACSettings, FrameworkType
+from mlagents.trainers.components.reward_signals import RewardSignal
+try:
+    from mlagents.trainers.policy.torch_policy import TorchPolicy
+    from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
+except ModuleNotFoundError:
+    TorchPolicy = None  # type: ignore
+    TorchSACOptimizer = None  # type: ignore
 
 logger = get_logger(__name__)
 
             agent_buffer_trajectory["environment_rewards"]
         )
         for name, reward_signal in self.optimizer.reward_signals.items():
-            evaluate_result = reward_signal.evaluate_batch(
-                agent_buffer_trajectory
-            ).scaled_reward
+            if isinstance(reward_signal, RewardSignal):
+                evaluate_result = reward_signal.evaluate_batch(
+                    agent_buffer_trajectory
+                ).scaled_reward
+            else:
+                evaluate_result = (
+                    reward_signal.evaluate(agent_buffer_trajectory)
+                    * reward_signal.strength
+                )
             # Report the reward signals
             self.collected_rewards[name][agent_id] += np.sum(evaluate_result)
 
         )
         for name, v in value_estimates.items():
-            self._stats_reporter.add_stat(
-                self.optimizer.reward_signals[name].value_name, np.mean(v)
-            )
+            if isinstance(self.optimizer.reward_signals[name], RewardSignal):
+                self._stats_reporter.add_stat(
+                    self.optimizer.reward_signals[name].value_name, np.mean(v)
+                )
+            else:
+                self._stats_reporter.add_stat(
+                    f"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value",
+                    np.mean(v),
+                )
 
         # Bootstrap using the last step rather than the bootstrap step if max step is reached.
         # Set last element to duplicate obs and remove dones.
         self._update_reward_signals()
         return policy_was_updated
 
-    def create_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
-    ) -> TFPolicy:
-        policy = TFPolicy(
-            self.seed,
-            behavior_spec,
-            self.trainer_settings,
-            self.artifact_path,
-            self.load,
-            tanh_squash=True,
-            reparameterize=True,
-            create_tf_graph=False,
-        )
+    def maybe_load_replay_buffer(self):
         # Load the replay buffer if load
         if self.load and self.checkpoint_replay_buffer:
             try:
                 )
             )
 
+    def create_tf_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TFPolicy:
+        policy = TFPolicy(
+            self.seed,
+            behavior_spec,
+            self.trainer_settings,
+            self.artifact_path,
+            self.load,
+            tanh_squash=True,
+            reparameterize=True,
+            create_tf_graph=False,
+        )
+        self.maybe_load_replay_buffer()
+        return policy
+
+    def create_torch_policy(
+        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+    ) -> TorchPolicy:
+        """
+        Creates a PPO policy to trainers list of policies.
+        :param parsed_behavior_id:
+        :param behavior_spec: specifications for policy construction
+        :return policy
+        """
+        policy = TorchPolicy(
+            self.seed,
+            behavior_spec,
+            self.trainer_settings,
+            self.artifact_path,
+            self.load,
+            condition_sigma_on_obs=True,
+            tanh_squash=True,
+            separate_critic=True,
+        )
+        self.maybe_load_replay_buffer()
         return policy
 
     def _update_sac_policy(self) -> bool:
                 )
                 # Get rewards for each reward
                 for name, signal in self.optimizer.reward_signals.items():
-                    sampled_minibatch[f"{name}_rewards"] = signal.evaluate_batch(
-                        sampled_minibatch
-                    ).scaled_reward
+                    if isinstance(signal, RewardSignal):
+                        sampled_minibatch[f"{name}_rewards"] = signal.evaluate_batch(
+                            sampled_minibatch
+                        ).scaled_reward
+                    else:
+                        sampled_minibatch[f"{name}_rewards"] = (
+                            signal.evaluate(sampled_minibatch) * signal.strength
+                        )
 
                 update_stats = self.optimizer.update(sampled_minibatch, n_sequences)
                 for stat_name, value in update_stats.items():
             reward_signal_minibatches = {}
             for name, signal in self.optimizer.reward_signals.items():
                 logger.debug(f"Updating {name} at step {self.step}")
-                # Some signals don't need a minibatch to be sampled - so we don't!
-                if signal.update_dict:
-                    reward_signal_minibatches[name] = buffer.sample_mini_batch(
-                        self.hyperparameters.batch_size,
-                        sequence_length=self.policy.sequence_length,
-                    )
+                if isinstance(signal, RewardSignal):
+                    # Some signals don't need a minibatch to be sampled - so we don't!
+                    if signal.update_dict:
+                        reward_signal_minibatches[name] = buffer.sample_mini_batch(
+                            self.hyperparameters.batch_size,
+                            sequence_length=self.policy.sequence_length,
+                        )
             update_stats = self.optimizer.update_reward_signals(
                 reward_signal_minibatches, n_sequences
             )
             )
         self.policy = policy
         self.policies[parsed_behavior_id.behavior_id] = policy
-        self.optimizer = SACOptimizer(
-            cast(TFPolicy, self.policy), self.trainer_settings
-        )
+        if self.framework == FrameworkType.PYTORCH:
+            self.optimizer = TorchSACOptimizer(  # type: ignore
+                self.policy, self.trainer_settings  # type: ignore
+            )  # type: ignore
+        else:
+            self.optimizer = SACOptimizer(  # type: ignore
+                self.policy, self.trainer_settings  # type: ignore
+            )  # type: ignore
         for _reward_signal in self.optimizer.reward_signals.keys():
             self.collected_rewards[_reward_signal] = defaultdict(lambda: 0)
         # Needed to resume loads properly

ml-agents/mlagents/trainers/tests/test_ppo.py

 from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
 
 from mlagents.trainers.ppo.trainer import PPOTrainer, discount_rewards
-from mlagents.trainers.ppo.optimizer import PPOOptimizer
+from mlagents.trainers.ppo.optimizer_tf import PPOOptimizer
 from mlagents.trainers.policy.tf_policy import TFPolicy
 from mlagents.trainers.agent_processor import AgentManagerQueue
 from mlagents.trainers.tests import mock_brain as mb

ml-agents/mlagents/trainers/tests/test_reward_signals.py

 import mlagents.trainers.tests.mock_brain as mb
 from mlagents.trainers.policy.tf_policy import TFPolicy
 from mlagents.trainers.sac.optimizer import SACOptimizer
-from mlagents.trainers.ppo.optimizer import PPOOptimizer
+from mlagents.trainers.ppo.optimizer_tf import PPOOptimizer
 from mlagents.trainers.tests.test_simple_rl import PPO_CONFIG, SAC_CONFIG
 from mlagents.trainers.settings import (
     GAILSettings,

ml-agents/mlagents/trainers/tests/test_rl_trainer.py

     def add_policy(self, mock_behavior_id, mock_policy):
         self.policies[mock_behavior_id] = mock_policy
 
-    def create_policy(self):
+    def create_tf_policy(self):
+        return mock.Mock()
+
+    def create_torch_policy(self):
         return mock.Mock()
 
     def _process_trajectory(self, trajectory):

com.unity.ml-agents/Runtime/SensorHelper.cs.meta

 fileFormatVersion: 2
-guid: 11fe037a02b4a483cb9342c3454232cd
+guid: 7c1189c0af42c46f7b533350d49ad3e7
 MonoImporter:
   externalObjects: {}
   serializedVersion: 2

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/OrientationCubeController.cs.meta

 fileFormatVersion: 2
-guid: fcb7a51f0d5f8404db7b85bd35ecc1fb
+guid: 771e78c5e980e440e8cd19716b55075f
 MonoImporter:
   externalObjects: {}
   serializedVersion: 2

ml-agents/mlagents/trainers/tf/models.py

         :param action_masks: The mask for the logits. Must be of dimension [None x total_number_of_action]
         :param action_size: A list containing the number of possible actions for each branch
         :return: The action output dimension [batch_size, num_branches], the concatenated
-            normalized probs (after softmax)
-        and the concatenated normalized log probs
+            normalized log_probs (after softmax)
+        and the concatenated normalized log log_probs
         """
         branch_masks = ModelUtils.break_into_branches(action_masks, action_size)
         raw_probs = [

Project/Assets/csc.rsp

+-warnaserror+
+-warnaserror-:618

utils/validate_release_links.py

+#!/usr/bin/env python3
+
+import ast
+import sys
+import os
+import re
+import subprocess
+from typing import List, Optional, Pattern
+
+RELEASE_PATTERN = re.compile(r"release_[0-9]+(_docs)*")
+TRAINER_INIT_FILE = "ml-agents/mlagents/trainers/__init__.py"
+
+# Filename -> regex list to allow specific lines.
+# To allow everything in the file, use None for the value
+ALLOW_LIST = {
+    # Previous release table
+    "README.md": re.compile(r"\*\*Release [0-9]+\*\*"),
+    "docs/Versioning.md": None,
+    "com.unity.ml-agents/CHANGELOG.md": None,
+    "utils/make_readme_table.py": None,
+    "utils/validate_release_links.py": None,
+}
+
+
+def test_pattern():
+    # Just some sanity check that the regex works as expected.
+    assert RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/release_4_docs/Food.md"
+    )
+    assert RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/release_4/Foo.md"
+    )
+    assert RELEASE_PATTERN.search(
+        "git clone --branch release_4 https://github.com/Unity-Technologies/ml-agents.git"
+    )
+    assert RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/release_123_docs/Foo.md"
+    )
+    assert RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/release_123/Foo.md"
+    )
+    assert not RELEASE_PATTERN.search(
+        "https://github.com/Unity-Technologies/ml-agents/blob/latest_release/docs/Foo.md"
+    )
+    print("tests OK!")
+
+
+def git_ls_files() -> List[str]:
+    """
+    Run "git ls-files" and return a list with one entry per line.
+    This returns the list of all files tracked by git.
+    """
+    return subprocess.check_output(["git", "ls-files"], universal_newlines=True).split(
+        "\n"
+    )
+
+
+def get_release_tag() -> Optional[str]:
+    """
+    Returns the release tag for the mlagents python package.
+    This will be None on the master branch.
+    :return:
+    """
+    with open(TRAINER_INIT_FILE) as f:
+        for line in f:
+            if "__release_tag__" in line:
+                lhs, equals_string, rhs = line.strip().partition(" = ")
+                # Evaluate the right hand side of the expression
+                return ast.literal_eval(rhs)
+    # If we couldn't find the release tag, raise an exception
+    # (since we can't return None here)
+    raise RuntimeError("Can't determine release tag")
+
+
+def check_file(filename: str, global_allow_pattern: Pattern) -> List[str]:
+    """
+    Validate a single file and return any offending lines.
+    """
+    bad_lines = []
+    with open(filename) as f:
+        for line in f:
+            if not RELEASE_PATTERN.search(line):
+                continue
+
+            if global_allow_pattern.search(line):
+                continue
+
+            if filename in ALLOW_LIST:
+                if ALLOW_LIST[filename] is None or ALLOW_LIST[filename].search(line):
+                    continue
+
+            bad_lines.append(f"{filename}: {line.strip()}")
+    return bad_lines
+
+
+def check_all_files(allow_pattern: Pattern) -> List[str]:
+    """
+    Validate all files tracked by git.
+    :param allow_pattern:
+    """
+    bad_lines = []
+    file_types = {".py", ".md", ".cs"}
+    for file_name in git_ls_files():
+        if "localized" in file_name or os.path.splitext(file_name)[1] not in file_types:
+            continue
+        bad_lines += check_file(file_name, allow_pattern)
+    return bad_lines
+
+
+def main():
+    release_tag = get_release_tag()
+    if not release_tag:
+        print("Release tag is None, exiting")
+        sys.exit(0)
+
+    print(f"Release tag: {release_tag}")
+    allow_pattern = re.compile(f"{release_tag}(_docs)*")
+    bad_lines = check_all_files(allow_pattern)
+    if bad_lines:
+        print(
+            f"Found lines referring to previous release. Either update the files, or add an exclusion to {__file__}"
+        )
+        for line in bad_lines:
+            print(line)
+
+    sys.exit(1 if bad_lines else 0)
+
+
+if __name__ == "__main__":
+    if "--test" in sys.argv:
+        test_pattern()
+    main()

experiment_torch.py

+import json
+import os
+import torch
+from mlagents.tf_utils import tf
+import argparse
+from mlagents.trainers.learn import run_cli, parse_command_line
+from mlagents.trainers.settings import TestingConfiguration
+from mlagents.trainers.stats import StatsReporter
+from mlagents_envs.timers import _thread_timer_stacks
+
+
+def run_experiment(
+    name: str,
+    steps: int,
+    use_torch: bool,
+    algo: str,
+    num_torch_threads: int,
+    use_gpu: bool,
+    num_envs: int = 1,
+    config_name=None,
+):
+    TestingConfiguration.env_name = name
+    TestingConfiguration.max_steps = steps
+    TestingConfiguration.use_torch = use_torch
+    TestingConfiguration.device = "cuda:0" if use_gpu else "cpu"
+    if use_gpu:
+        tf.device("/GPU:0")
+    else:
+        tf.device("/device:CPU:0")
+    if not torch.cuda.is_available() and use_gpu:
+        return (
+            name,
+            str(steps),
+            str(use_torch),
+            algo,
+            str(num_torch_threads),
+            str(num_envs),
+            str(use_gpu),
+            "na",
+            "na",
+            "na",
+            "na",
+            "na",
+            "na",
+            "na",
+        )
+    if config_name is None:
+        config_name = name
+    run_options = parse_command_line(
+        [f"config/{algo}/{config_name}.yaml", "--num-envs", f"{num_envs}"]
+    )
+    run_options.checkpoint_settings.run_id = (
+        f"{name}_test_" + str(steps) + "_" + ("torch" if use_torch else "tf")
+    )
+    run_options.checkpoint_settings.force = True
+    # run_options.env_settings.num_envs = num_envs
+    for trainer_settings in run_options.behaviors.values():
+        trainer_settings.threaded = False
+    timers_path = os.path.join(
+        "results", run_options.checkpoint_settings.run_id, "run_logs", "timers.json"
+    )
+    if use_torch:
+        torch.set_num_threads(num_torch_threads)
+    run_cli(run_options)
+    StatsReporter.writers.clear()
+    StatsReporter.stats_dict.clear()
+    _thread_timer_stacks.clear()
+    with open(timers_path) as timers_json_file:
+        timers_json = json.load(timers_json_file)
+        total = timers_json["total"]
+        tc_advance = timers_json["children"]["TrainerController.start_learning"][
+            "children"
+        ]["TrainerController.advance"]
+        evaluate = timers_json["children"]["TrainerController.start_learning"][
+            "children"
+        ]["TrainerController.advance"]["children"]["env_step"]["children"][
+            "SubprocessEnvManager._take_step"
+        ][
+            "children"
+        ]
+        update = timers_json["children"]["TrainerController.start_learning"][
+            "children"
+        ]["TrainerController.advance"]["children"]["trainer_advance"]["children"][
+            "_update_policy"
+        ][
+            "children"
+        ]
+        tc_advance_total = tc_advance["total"]
+        tc_advance_count = tc_advance["count"]
+    if use_torch:
+        if algo == "ppo":
+            update_total = update["TorchPPOOptimizer.update"]["total"]
+            update_count = update["TorchPPOOptimizer.update"]["count"]
+        else:
+            update_total = update["SACTrainer._update_policy"]["total"]
+            update_count = update["SACTrainer._update_policy"]["count"]
+        evaluate_total = evaluate["TorchPolicy.evaluate"]["total"]
+        evaluate_count = evaluate["TorchPolicy.evaluate"]["count"]
+    else:
+        if algo == "ppo":
+            update_total = update["PPOOptimizer.update"]["total"]
+            update_count = update["PPOOptimizer.update"]["count"]
+        else:
+            update_total = update["SACTrainer._update_policy"]["total"]
+            update_count = update["SACTrainer._update_policy"]["count"]
+        evaluate_total = evaluate["NNPolicy.evaluate"]["total"]
+        evaluate_count = evaluate["NNPolicy.evaluate"]["count"]
+    # todo: do total / count
+    return (
+        name,
+        str(steps),
+        str(use_torch),
+        algo,
+        str(num_torch_threads),
+        str(num_envs),
+        str(use_gpu),
+        str(total),
+        str(tc_advance_total),
+        str(tc_advance_count),
+        str(update_total),
+        str(update_count),
+        str(evaluate_total),
+        str(evaluate_count),
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--steps", default=25000, type=int, help="The number of steps")
+    parser.add_argument("--num-envs", default=1, type=int, help="The number of envs")
+    parser.add_argument(
+        "--gpu", default=False, action="store_true", help="If true, will use the GPU"
+    )
+    parser.add_argument(
+        "--threads",
+        default=False,
+        action="store_true",
+        help="If true, will try both 1 and 8 threads for torch",
+    )
+    parser.add_argument(
+        "--ball",
+        default=False,
+        action="store_true",
+        help="If true, will only do 3dball",
+    )
+    parser.add_argument(
+        "--sac",
+        default=False,
+        action="store_true",
+        help="If true, will run sac instead of ppo",
+    )
+    args = parser.parse_args()
+
+    if args.gpu:
+        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    else:
+        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+    algo = "ppo"
+    if args.sac:
+        algo = "sac"
+
+    envs_config_tuples = [
+        ("3DBall", "3DBall"),
+        ("GridWorld", "GridWorld"),
+        ("PushBlock", "PushBlock"),
+        ("CrawlerStaticTarget", "CrawlerStatic"),
+    ]
+    if algo == "ppo":
+        envs_config_tuples += [
+            ("Hallway", "Hallway"),
+            ("VisualHallway", "VisualHallway"),
+        ]
+    if args.ball:
+        envs_config_tuples = [("3DBall", "3DBall")]
+
+    labels = (
+        "name",
+        "steps",
+        "use_torch",
+        "algorithm",
+        "num_torch_threads",
+        "num_envs",
+        "use_gpu",
+        "total",
+        "tc_advance_total",
+        "tc_advance_count",
+        "update_total",
+        "update_count",
+        "evaluate_total",
+        "evaluate_count",
+    )
+
+    results = []
+    results.append(labels)
+    f = open(
+        f"result_data_steps_{args.steps}_algo_{algo}_envs_{args.num_envs}_gpu_{args.gpu}_thread_{args.threads}.txt",
+        "w",
+    )
+    f.write(" ".join(labels) + "\n")
+
+    for env_config in envs_config_tuples:
+        data = run_experiment(
+            name=env_config[0],
+            steps=args.steps,
+            use_torch=True,
+            algo=algo,
+            num_torch_threads=1,
+            use_gpu=args.gpu,
+            num_envs=args.num_envs,
+            config_name=env_config[1],
+        )
+        results.append(data)
+        f.write(" ".join(data) + "\n")
+
+        if args.threads:
+            data = run_experiment(
+                name=env_config[0],
+                steps=args.steps,
+                use_torch=True,
+                algo=algo,
+                num_torch_threads=8,
+                use_gpu=args.gpu,
+                num_envs=args.num_envs,
+                config_name=env_config[1],
+            )
+            results.append(data)
+            f.write(" ".join(data) + "\n")
+
+        data = run_experiment(
+            name=env_config[0],
+            steps=args.steps,
+            use_torch=False,
+            algo=algo,
+            num_torch_threads=1,
+            use_gpu=args.gpu,
+            num_envs=args.num_envs,
+            config_name=env_config[1],
+        )
+        results.append(data)
+        f.write(" ".join(data) + "\n")
+    for r in results:
+        print(*r)
+    f.close()
+
+
+if __name__ == "__main__":
+    main()

ml-agents/mlagents/trainers/optimizer/torch_optimizer.py

+from typing import Dict, Optional, Tuple, List
+import torch
+import numpy as np
+from mlagents_envs.base_env import DecisionSteps
+
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.torch.components.bc.module import BCModule
+from mlagents.trainers.torch.components.reward_providers import create_reward_provider
+
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.optimizer import Optimizer
+from mlagents.trainers.settings import TrainerSettings
+from mlagents.trainers.trajectory import SplitObservations
+from mlagents.trainers.torch.utils import ModelUtils
+
+
+class TorchOptimizer(Optimizer):  # pylint: disable=W0223
+    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
+        super().__init__()
+        self.policy = policy
+        self.trainer_settings = trainer_settings
+        self.update_dict: Dict[str, torch.Tensor] = {}
+        self.value_heads: Dict[str, torch.Tensor] = {}
+        self.memory_in: torch.Tensor = None
+        self.memory_out: torch.Tensor = None
+        self.m_size: int = 0
+        self.global_step = torch.tensor(0)
+        self.bc_module: Optional[BCModule] = None
+        self.create_reward_signals(trainer_settings.reward_signals)
+        if trainer_settings.behavioral_cloning is not None:
+            self.bc_module = BCModule(
+                self.policy,
+                trainer_settings.behavioral_cloning,
+                policy_learning_rate=trainer_settings.hyperparameters.learning_rate,
+                default_batch_size=trainer_settings.hyperparameters.batch_size,
+                default_num_epoch=3,
+            )
+
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        pass
+
+    def create_reward_signals(self, reward_signal_configs):
+        """
+        Create reward signals
+        :param reward_signal_configs: Reward signal config.
+        """
+        for reward_signal, settings in reward_signal_configs.items():
+            # Name reward signals by string in case we have duplicates later
+            self.reward_signals[reward_signal.value] = create_reward_provider(
+                reward_signal, self.policy.behavior_spec, settings
+            )
+
+    def get_value_estimates(
+        self, decision_requests: DecisionSteps, idx: int, done: bool
+    ) -> Dict[str, float]:
+        """
+        Generates value estimates for bootstrapping.
+        :param decision_requests:
+        :param idx: Index in BrainInfo of agent.
+        :param done: Whether or not this is the last element of the episode,
+        in which case the value estimate will be 0.
+        :return: The value estimate dictionary with key being the name of the reward signal
+        and the value the corresponding value estimate.
+        """
+        vec_vis_obs = SplitObservations.from_observations(decision_requests.obs)
+
+        value_estimates = self.policy.actor_critic.critic_pass(
+            np.expand_dims(vec_vis_obs.vector_observations[idx], 0),
+            np.expand_dims(vec_vis_obs.visual_observations[idx], 0),
+        )
+
+        value_estimates = {k: float(v) for k, v in value_estimates.items()}
+
+        # If we're done, reassign all of the value estimates that need terminal states.
+        if done:
+            for k in value_estimates:
+                if not self.reward_signals[k].ignore_done:
+                    value_estimates[k] = 0.0
+
+        return value_estimates
+
+    def get_trajectory_value_estimates(
+        self, batch: AgentBuffer, next_obs: List[np.ndarray], done: bool
+    ) -> Tuple[Dict[str, np.ndarray], Dict[str, float]]:
+        vector_obs = [ModelUtils.list_to_tensor(batch["vector_obs"])]
+        if self.policy.use_vis_obs:
+            visual_obs = []
+            for idx, _ in enumerate(
+                self.policy.actor_critic.network_body.visual_encoders
+            ):
+                visual_ob = ModelUtils.list_to_tensor(batch["visual_obs%d" % idx])
+                visual_obs.append(visual_ob)
+        else:
+            visual_obs = []
+
+        memory = torch.zeros([1, len(vector_obs[0]), self.policy.m_size])
+
+        next_obs = np.concatenate(next_obs, axis=-1)
+        next_obs = [ModelUtils.list_to_tensor(next_obs).unsqueeze(0)]
+        next_memory = torch.zeros([1, 1, self.policy.m_size])
+
+        value_estimates = self.policy.actor_critic.critic_pass(
+            vector_obs, visual_obs, memory
+        )
+
+        next_value_estimate = self.policy.actor_critic.critic_pass(
+            next_obs, next_obs, next_memory
+        )
+
+        for name, estimate in value_estimates.items():
+            value_estimates[name] = estimate.detach().cpu().numpy()
+            next_value_estimate[name] = next_value_estimate[name].detach().cpu().numpy()
+
+        if done:
+            for k in next_value_estimate:
+                if not self.reward_signals[k].ignore_done:
+                    next_value_estimate[k] = 0.0
+
+        return value_estimates, next_value_estimate

ml-agents/mlagents/trainers/policy/torch_policy.py

+from typing import Any, Dict, List
+import numpy as np
+import torch
+
+import os
+from torch import onnx
+from mlagents.model_serialization import SerializationSettings
+from mlagents.trainers.action_info import ActionInfo
+from mlagents.trainers.behavior_id_utils import get_global_agent_id
+
+from mlagents.trainers.policy import Policy
+from mlagents_envs.base_env import DecisionSteps, BehaviorSpec
+from mlagents_envs.timers import timed
+
+from mlagents.trainers.settings import TrainerSettings, TestingConfiguration
+from mlagents.trainers.trajectory import SplitObservations
+from mlagents.trainers.torch.networks import SharedActorCritic, SeparateActorCritic
+from mlagents.trainers.torch.utils import ModelUtils
+
+EPSILON = 1e-7  # Small value to avoid divide by zero
+
+
+class TorchPolicy(Policy):
+    def __init__(
+        self,
+        seed: int,
+        behavior_spec: BehaviorSpec,
+        trainer_settings: TrainerSettings,
+        model_path: str,
+        load: bool = False,
+        tanh_squash: bool = False,
+        reparameterize: bool = False,
+        separate_critic: bool = True,
+        condition_sigma_on_obs: bool = True,
+    ):
+        """
+        Policy that uses a multilayer perceptron to map the observations to actions. Could
+        also use a CNN to encode visual input prior to the MLP. Supports discrete and
+        continuous action spaces, as well as recurrent networks.
+        :param seed: Random seed.
+        :param brain: Assigned BrainParameters object.
+        :param trainer_settings: Defined training parameters.
+        :param load: Whether a pre-trained model will be loaded or a new one created.
+        :param tanh_squash: Whether to use a tanh function on the continuous output,
+        or a clipped output.
+        :param reparameterize: Whether we are using the resampling trick to update the policy
+        in continuous output.
+        """
+        super().__init__(
+            seed,
+            behavior_spec,
+            trainer_settings,
+            model_path,
+            load,
+            tanh_squash,
+            reparameterize,
+            condition_sigma_on_obs,
+        )
+        self.global_step = 0
+        self.grads = None
+        if TestingConfiguration.device != "cpu":
+            torch.set_default_tensor_type(torch.cuda.FloatTensor)
+        else:
+            torch.set_default_tensor_type(torch.FloatTensor)
+
+        reward_signal_configs = trainer_settings.reward_signals
+        reward_signal_names = [key.value for key, _ in reward_signal_configs.items()]
+
+        self.stats_name_to_update_name = {
+            "Losses/Value Loss": "value_loss",
+            "Losses/Policy Loss": "policy_loss",
+        }
+        if separate_critic:
+            ac_class = SeparateActorCritic
+        else:
+            ac_class = SharedActorCritic
+        self.actor_critic = ac_class(
+            observation_shapes=self.behavior_spec.observation_shapes,
+            network_settings=trainer_settings.network_settings,
+            act_type=behavior_spec.action_type,
+            act_size=self.act_size,
+            stream_names=reward_signal_names,
+            conditional_sigma=self.condition_sigma_on_obs,
+            tanh_squash=tanh_squash,
+        )
+
+        self.actor_critic.to(TestingConfiguration.device)
+
+    def split_decision_step(self, decision_requests):
+        vec_vis_obs = SplitObservations.from_observations(decision_requests.obs)
+        mask = None
+        if not self.use_continuous_act:
+            mask = torch.ones([len(decision_requests), np.sum(self.act_size)])
+            if decision_requests.action_mask is not None:
+                mask = torch.as_tensor(
+                    1 - np.concatenate(decision_requests.action_mask, axis=1)
+                )
+        return vec_vis_obs.vector_observations, vec_vis_obs.visual_observations, mask
+
+    def update_normalization(self, vector_obs: np.ndarray) -> None:
+        """
+        If this policy normalizes vector observations, this will update the norm values in the graph.
+        :param vector_obs: The vector observations to add to the running estimate of the distribution.
+        """
+        vector_obs = [torch.as_tensor(vector_obs)]
+        if self.use_vec_obs and self.normalize:
+            self.actor_critic.update_normalization(vector_obs)
+
+    @timed
+    def sample_actions(
+        self,
+        vec_obs,
+        vis_obs,
+        masks=None,
+        memories=None,
+        seq_len=1,
+        all_log_probs=False,
+    ):
+        """
+        :param all_log_probs: Returns (for discrete actions) a tensor of log probs, one for each action.
+        """
+        dists, value_heads, memories = self.actor_critic.get_dist_and_value(
+            vec_obs, vis_obs, masks, memories, seq_len
+        )
+        action_list = self.actor_critic.sample_action(dists)
+        log_probs, entropies, all_logs = ModelUtils.get_probs_and_entropy(
+            action_list, dists
+        )
+        actions = torch.stack(action_list, dim=-1)
+        if self.use_continuous_act:
+            actions = actions[:, :, 0]
+        else:
+            actions = actions[:, 0, :]
+
+        return (
+            actions,
+            all_logs if all_log_probs else log_probs,
+            entropies,
+            value_heads,
+            memories,
+        )
+
+    def evaluate_actions(
+        self, vec_obs, vis_obs, actions, masks=None, memories=None, seq_len=1
+    ):
+        dists, value_heads, _ = self.actor_critic.get_dist_and_value(
+            vec_obs, vis_obs, masks, memories, seq_len
+        )
+        if len(actions.shape) <= 2:
+            actions = actions.unsqueeze(-1)
+        action_list = [actions[..., i] for i in range(actions.shape[2])]
+        log_probs, entropies, _ = ModelUtils.get_probs_and_entropy(action_list, dists)
+
+        return log_probs, entropies, value_heads
+
+    @timed
+    def evaluate(
+        self, decision_requests: DecisionSteps, global_agent_ids: List[str]
+    ) -> Dict[str, Any]:
+        """
+        Evaluates policy for the agent experiences provided.
+        :param global_agent_ids:
+        :param decision_requests: DecisionStep object containing inputs.
+        :return: Outputs from network as defined by self.inference_dict.
+        """
+        vec_obs, vis_obs, masks = self.split_decision_step(decision_requests)
+        vec_obs = [torch.as_tensor(vec_obs)]
+        vis_obs = [torch.as_tensor(vis_ob) for vis_ob in vis_obs]
+        memories = torch.as_tensor(self.retrieve_memories(global_agent_ids)).unsqueeze(
+            0
+        )
+
+        run_out = {}
+        with torch.no_grad():
+            action, log_probs, entropy, value_heads, memories = self.sample_actions(
+                vec_obs, vis_obs, masks=masks, memories=memories
+            )
+        run_out["action"] = action.detach().cpu().numpy()
+        run_out["pre_action"] = action.detach().cpu().numpy()
+        # Todo - make pre_action difference
+        run_out["log_probs"] = log_probs.detach().cpu().numpy()
+        run_out["entropy"] = entropy.detach().cpu().numpy()
+        run_out["value_heads"] = {
+            name: t.detach().cpu().numpy() for name, t in value_heads.items()
+        }
+        run_out["value"] = np.mean(list(run_out["value_heads"].values()), 0)
+        run_out["learning_rate"] = 0.0
+        if self.use_recurrent:
+            run_out["memories"] = memories.detach().cpu().numpy()
+        return run_out
+
+    def get_action(
+        self, decision_requests: DecisionSteps, worker_id: int = 0
+    ) -> ActionInfo:
+        """
+        Decides actions given observations information, and takes them in environment.
+        :param worker_id:
+        :param decision_requests: A dictionary of brain names and BrainInfo from environment.
+        :return: an ActionInfo containing action, memories, values and an object
+        to be passed to add experiences
+        """
+        if len(decision_requests) == 0:
+            return ActionInfo.empty()
+
+        global_agent_ids = [
+            get_global_agent_id(worker_id, int(agent_id))
+            for agent_id in decision_requests.agent_id
+        ]  # For 1-D array, the iterator order is correct.
+
+        run_out = self.evaluate(
+            decision_requests, global_agent_ids
+        )  # pylint: disable=assignment-from-no-return
+        self.save_memories(global_agent_ids, run_out.get("memory_out"))
+        return ActionInfo(
+            action=run_out.get("action"),
+            value=run_out.get("value"),
+            outputs=run_out,
+            agent_ids=list(decision_requests.agent_id),
+        )
+
+    def checkpoint(self, checkpoint_path: str, settings: SerializationSettings) -> None:
+        """
+        Checkpoints the policy on disk.
+
+        :param checkpoint_path: filepath to write the checkpoint
+        :param settings: SerializationSettings for exporting the model.
+        """
+        if not os.path.exists(self.model_path):
+            os.makedirs(self.model_path)
+        torch.save(self.actor_critic.state_dict(), f"{checkpoint_path}.pt")
+
+    def save(self, output_filepath: str, settings: SerializationSettings) -> None:
+        self.export_model(self.global_step)
+
+    def load_model(self, step=0):  # TODO: this doesn't work
+        load_path = self.model_path + "/model-" + str(step) + ".pt"
+        self.actor_critic.load_state_dict(torch.load(load_path))
+
+    def export_model(self, step=0):
+        fake_vec_obs = [torch.zeros([1] + [self.vec_obs_size])]
+        fake_vis_obs = [torch.zeros([1] + [84, 84, 3])]
+        fake_masks = torch.ones([1] + self.actor_critic.act_size)
+        # fake_memories = torch.zeros([1] + [self.m_size])
+        export_path = "./model-" + str(step) + ".onnx"
+        output_names = ["action", "action_probs"]
+        input_names = ["vector_observation", "action_mask"]
+        dynamic_axes = {"vector_observation": [0], "action": [0], "action_probs": [0]}
+        onnx.export(
+            self.actor_critic,
+            (fake_vec_obs, fake_vis_obs, fake_masks),
+            export_path,
+            verbose=True,
+            opset_version=12,
+            input_names=input_names,
+            output_names=output_names,
+            dynamic_axes=dynamic_axes,
+        )
+
+    @property
+    def use_vis_obs(self):
+        return self.vis_obs_size > 0
+
+    @property
+    def use_vec_obs(self):
+        return self.vec_obs_size > 0
+
+    def get_current_step(self):
+        """
+        Gets current model step.
+        :return: current model step.
+        """
+        step = self.global_step
+        return step
+
+    def increment_step(self, n_steps):
+        """
+        Increments model step.
+        """
+        self.global_step += n_steps
+        return self.get_current_step()
+
+    def load_weights(self, values: List[np.ndarray]) -> None:
+        pass
+
+    def init_load_weights(self) -> None:
+        pass
+
+    def get_weights(self) -> List[np.ndarray]:
+        return []

ml-agents/mlagents/trainers/ppo/optimizer_torch.py

+from typing import Dict, cast
+import torch
+
+from mlagents.trainers.buffer import AgentBuffer
+
+from mlagents_envs.timers import timed
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.settings import TrainerSettings, PPOSettings
+from mlagents.trainers.torch.utils import ModelUtils
+
+
+class TorchPPOOptimizer(TorchOptimizer):
+    def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
+        """
+        Takes a Policy and a Dict of trainer parameters and creates an Optimizer around the policy.
+        The PPO optimizer has a value estimator and a loss function.
+        :param policy: A TFPolicy object that will be updated by this PPO Optimizer.
+        :param trainer_params: Trainer parameters dictionary that specifies the
+        properties of the trainer.
+        """
+        # Create the graph here to give more granular control of the TF graph to the Optimizer.
+
+        super().__init__(policy, trainer_settings)
+        params = list(self.policy.actor_critic.parameters())
+        self.hyperparameters: PPOSettings = cast(
+            PPOSettings, trainer_settings.hyperparameters
+        )
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            self.hyperparameters.learning_rate_schedule,
+            self.hyperparameters.learning_rate,
+            1e-10,
+            self.trainer_settings.max_steps,
+        )
+        self.decay_epsilon = ModelUtils.DecayedValue(
+            self.hyperparameters.learning_rate_schedule,
+            self.hyperparameters.epsilon,
+            0.1,
+            self.trainer_settings.max_steps,
+        )
+        self.decay_beta = ModelUtils.DecayedValue(
+            self.hyperparameters.learning_rate_schedule,
+            self.hyperparameters.beta,
+            1e-5,
+            self.trainer_settings.max_steps,
+        )
+
+        self.optimizer = torch.optim.Adam(
+            params, lr=self.trainer_settings.hyperparameters.learning_rate
+        )
+        self.stats_name_to_update_name = {
+            "Losses/Value Loss": "value_loss",
+            "Losses/Policy Loss": "policy_loss",
+        }
+
+        self.stream_names = list(self.reward_signals.keys())
+
+    def ppo_value_loss(
+        self,
+        values: Dict[str, torch.Tensor],
+        old_values: Dict[str, torch.Tensor],
+        returns: Dict[str, torch.Tensor],
+        epsilon: float,
+    ) -> torch.Tensor:
+        """
+        Creates training-specific Tensorflow ops for PPO models.
+        :param returns:
+        :param old_values:
+        :param values:
+        """
+        value_losses = []
+        for name, head in values.items():
+            old_val_tensor = old_values[name]
+            returns_tensor = returns[name]
+            clipped_value_estimate = old_val_tensor + torch.clamp(
+                head - old_val_tensor, -1 * epsilon, epsilon
+            )
+            v_opt_a = (returns_tensor - head) ** 2
+            v_opt_b = (returns_tensor - clipped_value_estimate) ** 2
+            value_loss = torch.mean(torch.max(v_opt_a, v_opt_b))
+            value_losses.append(value_loss)
+        value_loss = torch.mean(torch.stack(value_losses))
+        return value_loss
+
+    def ppo_policy_loss(self, advantages, log_probs, old_log_probs, masks):
+        """
+        Creates training-specific Tensorflow ops for PPO models.
+        :param masks:
+        :param advantages:
+        :param log_probs: Current policy probabilities
+        :param old_log_probs: Past policy probabilities
+        """
+        advantage = advantages.unsqueeze(-1)
+
+        decay_epsilon = self.hyperparameters.epsilon
+
+        r_theta = torch.exp(log_probs - old_log_probs)
+        p_opt_a = r_theta * advantage
+        p_opt_b = (
+            torch.clamp(r_theta, 1.0 - decay_epsilon, 1.0 + decay_epsilon) * advantage
+        )
+        policy_loss = -torch.mean(torch.min(p_opt_a, p_opt_b))
+        return policy_loss
+
+    @timed
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        """
+        Performs update on model.
+        :param batch: Batch of experiences.
+        :param num_sequences: Number of sequences to process.
+        :return: Results of update.
+        """
+        # Get decayed parameters
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        decay_eps = self.decay_epsilon.get_value(self.policy.get_current_step())
+        decay_bet = self.decay_beta.get_value(self.policy.get_current_step())
+        returns = {}
+        old_values = {}
+        for name in self.reward_signals:
+            old_values[name] = ModelUtils.list_to_tensor(
+                batch[f"{name}_value_estimates"]
+            )
+            returns[name] = ModelUtils.list_to_tensor(batch[f"{name}_returns"])
+
+        vec_obs = [ModelUtils.list_to_tensor(batch["vector_obs"])]
+        act_masks = ModelUtils.list_to_tensor(batch["action_mask"])
+        if self.policy.use_continuous_act:
+            actions = ModelUtils.list_to_tensor(batch["actions"]).unsqueeze(-1)
+        else:
+            actions = ModelUtils.list_to_tensor(batch["actions"], dtype=torch.long)
+
+        memories = [
+            ModelUtils.list_to_tensor(batch["memory"][i])
+            for i in range(0, len(batch["memory"]), self.policy.sequence_length)
+        ]
+        if len(memories) > 0:
+            memories = torch.stack(memories).unsqueeze(0)
+
+        if self.policy.use_vis_obs:
+            vis_obs = []
+            for idx, _ in enumerate(
+                self.policy.actor_critic.network_body.visual_encoders
+            ):
+                vis_ob = ModelUtils.list_to_tensor(batch["visual_obs%d" % idx])
+                vis_obs.append(vis_ob)
+        else:
+            vis_obs = []
+        log_probs, entropy, values = self.policy.evaluate_actions(
+            vec_obs,
+            vis_obs,
+            masks=act_masks,
+            actions=actions,
+            memories=memories,
+            seq_len=self.policy.sequence_length,
+        )
+        value_loss = self.ppo_value_loss(values, old_values, returns, decay_eps)
+        policy_loss = self.ppo_policy_loss(
+            ModelUtils.list_to_tensor(batch["advantages"]),
+            log_probs,
+            ModelUtils.list_to_tensor(batch["action_probs"]),
+            ModelUtils.list_to_tensor(batch["masks"], dtype=torch.int32),
+        )
+        loss = policy_loss + 0.5 * value_loss - decay_bet * torch.mean(entropy)
+
+        # Set optimizer learning rate
+        ModelUtils.update_learning_rate(self.optimizer, decay_lr)
+        self.optimizer.zero_grad()
+        loss.backward()
+
+        self.optimizer.step()
+        update_stats = {
+            "Losses/Policy Loss": abs(policy_loss.detach().cpu().numpy()),
+            "Losses/Value Loss": value_loss.detach().cpu().numpy(),
+            "Policy/Learning Rate": decay_lr,
+            "Policy/Epsilon": decay_eps,
+            "Policy/Beta": decay_bet,
+        }
+
+        for reward_provider in self.reward_signals.values():
+            update_stats.update(reward_provider.update(batch))
+
+        return update_stats

ml-agents/mlagents/trainers/sac/optimizer_torch.py

+import numpy as np
+from typing import Dict, List, Mapping, cast, Tuple
+import torch
+from torch import nn
+
+from mlagents_envs.logging_util import get_logger
+from mlagents_envs.base_env import ActionType
+from mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.torch.networks import ValueNetwork
+from mlagents.trainers.torch.utils import ModelUtils
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents_envs.timers import timed
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents.trainers.settings import TrainerSettings, SACSettings
+
+EPSILON = 1e-6  # Small value to avoid divide by zero
+
+logger = get_logger(__name__)
+
+
+class TorchSACOptimizer(TorchOptimizer):
+    class PolicyValueNetwork(nn.Module):
+        def __init__(
+            self,
+            stream_names: List[str],
+            observation_shapes: List[Tuple[int, ...]],
+            network_settings: NetworkSettings,
+            act_type: ActionType,
+            act_size: List[int],
+        ):
+            super().__init__()
+            if act_type == ActionType.CONTINUOUS:
+                num_value_outs = 1
+                num_action_ins = sum(act_size)
+            else:
+                num_value_outs = sum(act_size)
+                num_action_ins = 0
+            self.q1_network = ValueNetwork(
+                stream_names,
+                observation_shapes,
+                network_settings,
+                num_action_ins,
+                num_value_outs,
+            )
+            self.q2_network = ValueNetwork(
+                stream_names,
+                observation_shapes,
+                network_settings,
+                num_action_ins,
+                num_value_outs,
+            )
+
+        def forward(
+            self,
+            vec_inputs: List[torch.Tensor],
+            vis_inputs: List[torch.Tensor],
+            actions: torch.Tensor = None,
+        ) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]:
+            q1_out, _ = self.q1_network(vec_inputs, vis_inputs, actions=actions)
+            q2_out, _ = self.q2_network(vec_inputs, vis_inputs, actions=actions)
+            return q1_out, q2_out
+
+    def __init__(self, policy: TorchPolicy, trainer_params: TrainerSettings):
+        super().__init__(policy, trainer_params)
+        hyperparameters: SACSettings = cast(SACSettings, trainer_params.hyperparameters)
+        self.tau = hyperparameters.tau
+        self.init_entcoef = hyperparameters.init_entcoef
+
+        self.policy = policy
+        self.act_size = policy.act_size
+        policy_network_settings = policy.network_settings
+
+        self.tau = hyperparameters.tau
+        self.burn_in_ratio = 0.0
+
+        # Non-exposed SAC parameters
+        self.discrete_target_entropy_scale = 0.2  # Roughly equal to e-greedy 0.05
+        self.continuous_target_entropy_scale = 1.0
+
+        self.stream_names = list(self.reward_signals.keys())
+        # Use to reduce "survivor bonus" when using Curiosity or GAIL.
+        self.gammas = [_val.gamma for _val in trainer_params.reward_signals.values()]
+        self.use_dones_in_backup = {
+            name: int(not self.reward_signals[name].ignore_done)
+            for name in self.stream_names
+        }
+
+        self.value_network = TorchSACOptimizer.PolicyValueNetwork(
+            self.stream_names,
+            self.policy.behavior_spec.observation_shapes,
+            policy_network_settings,
+            self.policy.behavior_spec.action_type,
+            self.act_size,
+        )
+        self.target_network = ValueNetwork(
+            self.stream_names,
+            self.policy.behavior_spec.observation_shapes,
+            policy_network_settings,
+        )
+        self.soft_update(self.policy.actor_critic.critic, self.target_network, 1.0)
+
+        self._log_ent_coef = torch.nn.Parameter(
+            torch.log(torch.as_tensor([self.init_entcoef] * len(self.act_size))),
+            requires_grad=True,
+        )
+        if self.policy.use_continuous_act:
+            self.target_entropy = torch.as_tensor(
+                -1
+                * self.continuous_target_entropy_scale
+                * np.prod(self.act_size[0]).astype(np.float32)
+            )
+        else:
+            self.target_entropy = [
+                self.discrete_target_entropy_scale * np.log(i).astype(np.float32)
+                for i in self.act_size
+            ]
+
+        policy_params = list(self.policy.actor_critic.network_body.parameters()) + list(
+            self.policy.actor_critic.distribution.parameters()
+        )
+        value_params = list(self.value_network.parameters()) + list(
+            self.policy.actor_critic.critic.parameters()
+        )
+
+        logger.debug("value_vars")
+        for param in value_params:
+            logger.debug(param.shape)
+        logger.debug("policy_vars")
+        for param in policy_params:
+            logger.debug(param.shape)
+
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            hyperparameters.learning_rate_schedule,
+            hyperparameters.learning_rate,
+            1e-10,
+            self.trainer_settings.max_steps,
+        )
+        self.policy_optimizer = torch.optim.Adam(
+            policy_params, lr=hyperparameters.learning_rate
+        )
+        self.value_optimizer = torch.optim.Adam(
+            value_params, lr=hyperparameters.learning_rate
+        )
+        self.entropy_optimizer = torch.optim.Adam(
+            [self._log_ent_coef], lr=hyperparameters.learning_rate
+        )
+
+    def sac_q_loss(
+        self,
+        q1_out: Dict[str, torch.Tensor],
+        q2_out: Dict[str, torch.Tensor],
+        target_values: Dict[str, torch.Tensor],
+        dones: torch.Tensor,
+        rewards: Dict[str, torch.Tensor],
+        loss_masks: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        q1_losses = []
+        q2_losses = []
+        # Multiple q losses per stream
+        for i, name in enumerate(q1_out.keys()):
+            q1_stream = q1_out[name].squeeze()
+            q2_stream = q2_out[name].squeeze()
+            with torch.no_grad():
+                q_backup = rewards[name] + (
+                    (1.0 - self.use_dones_in_backup[name] * dones)
+                    * self.gammas[i]
+                    * target_values[name]
+                )
+            _q1_loss = 0.5 * torch.mean(
+                loss_masks * torch.nn.functional.mse_loss(q_backup, q1_stream)
+            )
+            _q2_loss = 0.5 * torch.mean(
+                loss_masks * torch.nn.functional.mse_loss(q_backup, q2_stream)
+            )
+
+            q1_losses.append(_q1_loss)
+            q2_losses.append(_q2_loss)
+        q1_loss = torch.mean(torch.stack(q1_losses))
+        q2_loss = torch.mean(torch.stack(q2_losses))
+        return q1_loss, q2_loss
+
+    def soft_update(self, source: nn.Module, target: nn.Module, tau: float) -> None:
+        for source_param, target_param in zip(source.parameters(), target.parameters()):
+            target_param.data.copy_(
+                target_param.data * (1.0 - tau) + source_param.data * tau
+            )
+
+    def sac_value_loss(
+        self,
+        log_probs: torch.Tensor,
+        values: Dict[str, torch.Tensor],
+        q1p_out: Dict[str, torch.Tensor],
+        q2p_out: Dict[str, torch.Tensor],
+        loss_masks: torch.Tensor,
+        discrete: bool,
+    ) -> torch.Tensor:
+        min_policy_qs = {}
+        with torch.no_grad():
+            _ent_coef = torch.exp(self._log_ent_coef)
+        for name in values.keys():
+            if not discrete:
+                min_policy_qs[name] = torch.min(q1p_out[name], q2p_out[name])
+            else:
+                action_probs = log_probs.exp()
+                _branched_q1p = ModelUtils.break_into_branches(
+                    q1p_out[name] * action_probs, self.act_size
+                )
+                _branched_q2p = ModelUtils.break_into_branches(
+                    q2p_out[name] * action_probs, self.act_size
+                )
+                _q1p_mean = torch.mean(
+                    torch.stack(
+                        [torch.sum(_br, dim=1, keepdim=True) for _br in _branched_q1p]
+                    ),
+                    dim=0,
+                )
+                _q2p_mean = torch.mean(
+                    torch.stack(
+                        [torch.sum(_br, dim=1, keepdim=True) for _br in _branched_q2p]
+                    ),
+                    dim=0,
+                )
+
+                min_policy_qs[name] = torch.min(_q1p_mean, _q2p_mean)
+
+        value_losses = []
+        if not discrete:
+            for name in values.keys():
+                with torch.no_grad():
+                    v_backup = min_policy_qs[name] - torch.sum(
+                        _ent_coef * log_probs, dim=1
+                    )
+                # print(log_probs, v_backup, _ent_coef, loss_masks)
+                value_loss = 0.5 * torch.mean(
+                    loss_masks * torch.nn.functional.mse_loss(values[name], v_backup)
+                )
+                value_losses.append(value_loss)
+        else:
+            branched_per_action_ent = ModelUtils.break_into_branches(
+                log_probs * log_probs.exp(), self.act_size
+            )
+            # We have to do entropy bonus per action branch
+            branched_ent_bonus = torch.stack(
+                [
+                    torch.sum(_ent_coef[i] * _lp, dim=1, keepdim=True)
+                    for i, _lp in enumerate(branched_per_action_ent)
+                ]
+            )
+            for name in values.keys():
+                with torch.no_grad():
+                    v_backup = min_policy_qs[name] - torch.mean(
+                        branched_ent_bonus, axis=0
+                    )
+                value_loss = 0.5 * torch.mean(
+                    loss_masks
+                    * torch.nn.functional.mse_loss(values[name], v_backup.squeeze())
+                )
+                value_losses.append(value_loss)
+        value_loss = torch.mean(torch.stack(value_losses))
+        if torch.isinf(value_loss).any() or torch.isnan(value_loss).any():
+            raise UnityTrainerException("Inf found")
+        return value_loss
+
+    def sac_policy_loss(
+        self,
+        log_probs: torch.Tensor,
+        q1p_outs: Dict[str, torch.Tensor],
+        loss_masks: torch.Tensor,
+        discrete: bool,
+    ) -> torch.Tensor:
+        _ent_coef = torch.exp(self._log_ent_coef)
+        mean_q1 = torch.mean(torch.stack(list(q1p_outs.values())), axis=0)
+        if not discrete:
+            mean_q1 = mean_q1.unsqueeze(1)
+            batch_policy_loss = torch.mean(_ent_coef * log_probs - mean_q1, dim=1)
+            policy_loss = torch.mean(loss_masks * batch_policy_loss)
+        else:
+            action_probs = log_probs.exp()
+            branched_per_action_ent = ModelUtils.break_into_branches(
+                log_probs * action_probs, self.act_size
+            )
+            branched_q_term = ModelUtils.break_into_branches(
+                mean_q1 * action_probs, self.act_size
+            )
+            branched_policy_loss = torch.stack(
+                [
+                    torch.sum(_ent_coef[i] * _lp - _qt, dim=1, keepdim=True)
+                    for i, (_lp, _qt) in enumerate(
+                        zip(branched_per_action_ent, branched_q_term)
+                    )
+                ]
+            )
+            batch_policy_loss = torch.squeeze(branched_policy_loss)
+        policy_loss = torch.mean(loss_masks * batch_policy_loss)
+        return policy_loss
+
+    def sac_entropy_loss(
+        self, log_probs: torch.Tensor, loss_masks: torch.Tensor, discrete: bool
+    ) -> torch.Tensor:
+        if not discrete:
+            with torch.no_grad():
+                target_current_diff = torch.sum(log_probs + self.target_entropy, dim=1)
+            entropy_loss = -torch.mean(
+                self._log_ent_coef * loss_masks * target_current_diff
+            )
+        else:
+            with torch.no_grad():
+                branched_per_action_ent = ModelUtils.break_into_branches(
+                    log_probs * log_probs.exp(), self.act_size
+                )
+                target_current_diff_branched = torch.stack(
+                    [
+                        torch.sum(_lp, axis=1, keepdim=True) + _te
+                        for _lp, _te in zip(
+                            branched_per_action_ent, self.target_entropy
+                        )
+                    ],
+                    axis=1,
+                )
+                target_current_diff = torch.squeeze(
+                    target_current_diff_branched, axis=2
+                )
+            entropy_loss = -torch.mean(
+                loss_masks
+                * torch.mean(self._log_ent_coef * target_current_diff, axis=1)
+            )
+
+        return entropy_loss
+
+    def _condense_q_streams(
+        self, q_output: Dict[str, torch.Tensor], discrete_actions: torch.Tensor
+    ) -> Dict[str, torch.Tensor]:
+        condensed_q_output = {}
+        onehot_actions = ModelUtils.actions_to_onehot(discrete_actions, self.act_size)
+        for key, item in q_output.items():
+            branched_q = ModelUtils.break_into_branches(item, self.act_size)
+            only_action_qs = torch.stack(
+                [
+                    torch.sum(_act * _q, dim=1, keepdim=True)
+                    for _act, _q in zip(onehot_actions, branched_q)
+                ]
+            )
+
+            condensed_q_output[key] = torch.mean(only_action_qs, dim=0)
+        return condensed_q_output
+
+    @timed
+    def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
+        """
+        Updates model using buffer.
+        :param num_sequences: Number of trajectories in batch.
+        :param batch: Experience mini-batch.
+        :param update_target: Whether or not to update target value network
+        :param reward_signal_batches: Minibatches to use for updating the reward signals,
+            indexed by name. If none, don't update the reward signals.
+        :return: Output from update process.
+        """
+        rewards = {}
+        for name in self.reward_signals:
+            rewards[name] = ModelUtils.list_to_tensor(batch[f"{name}_rewards"])
+
+        vec_obs = [ModelUtils.list_to_tensor(batch["vector_obs"])]
+        next_vec_obs = [ModelUtils.list_to_tensor(batch["next_vector_in"])]
+        act_masks = ModelUtils.list_to_tensor(batch["action_mask"])
+        if self.policy.use_continuous_act:
+            actions = ModelUtils.list_to_tensor(batch["actions"]).unsqueeze(-1)
+        else:
+            actions = ModelUtils.list_to_tensor(batch["actions"], dtype=torch.long)
+
+        memories = [
+            ModelUtils.list_to_tensor(batch["memory"][i])
+            for i in range(0, len(batch["memory"]), self.policy.sequence_length)
+        ]
+        if len(memories) > 0:
+            memories = torch.stack(memories).unsqueeze(0)
+        vis_obs: List[torch.Tensor] = []
+        next_vis_obs: List[torch.Tensor] = []
+        if self.policy.use_vis_obs:
+            vis_obs = []
+            for idx, _ in enumerate(
+                self.policy.actor_critic.network_body.visual_encoders
+            ):
+                vis_ob = ModelUtils.list_to_tensor(batch["visual_obs%d" % idx])
+                vis_obs.append(vis_ob)
+                next_vis_ob = ModelUtils.list_to_tensor(
+                    batch["next_visual_obs%d" % idx]
+                )
+                next_vis_obs.append(next_vis_ob)
+
+        # Copy normalizers from policy
+        self.value_network.q1_network.network_body.copy_normalization(
+            self.policy.actor_critic.network_body
+        )
+        self.value_network.q2_network.network_body.copy_normalization(
+            self.policy.actor_critic.network_body
+        )
+        self.target_network.network_body.copy_normalization(
+            self.policy.actor_critic.network_body
+        )
+        (
+            sampled_actions,
+            log_probs,
+            entropies,
+            sampled_values,
+            _,
+        ) = self.policy.sample_actions(
+            vec_obs,
+            vis_obs,
+            masks=act_masks,
+            memories=memories,
+            seq_len=self.policy.sequence_length,
+            all_log_probs=not self.policy.use_continuous_act,
+        )
+        if self.policy.use_continuous_act:
+            squeezed_actions = actions.squeeze(-1)
+            q1p_out, q2p_out = self.value_network(vec_obs, vis_obs, sampled_actions)
+            q1_out, q2_out = self.value_network(vec_obs, vis_obs, squeezed_actions)
+            q1_stream, q2_stream = q1_out, q2_out
+        else:
+            with torch.no_grad():
+                q1p_out, q2p_out = self.value_network(vec_obs, vis_obs)
+            q1_out, q2_out = self.value_network(vec_obs, vis_obs)
+            q1_stream = self._condense_q_streams(q1_out, actions)
+            q2_stream = self._condense_q_streams(q2_out, actions)
+
+        with torch.no_grad():
+            target_values, _ = self.target_network(next_vec_obs, next_vis_obs)
+        masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.int32)
+        use_discrete = not self.policy.use_continuous_act
+        dones = ModelUtils.list_to_tensor(batch["done"])
+
+        q1_loss, q2_loss = self.sac_q_loss(
+            q1_stream, q2_stream, target_values, dones, rewards, masks
+        )
+        value_loss = self.sac_value_loss(
+            log_probs, sampled_values, q1p_out, q2p_out, masks, use_discrete
+        )
+        policy_loss = self.sac_policy_loss(log_probs, q1p_out, masks, use_discrete)
+        entropy_loss = self.sac_entropy_loss(log_probs, masks, use_discrete)
+
+        total_value_loss = q1_loss + q2_loss + value_loss
+
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        ModelUtils.update_learning_rate(self.policy_optimizer, decay_lr)
+        self.policy_optimizer.zero_grad()
+        policy_loss.backward()
+        self.policy_optimizer.step()
+
+        ModelUtils.update_learning_rate(self.value_optimizer, decay_lr)
+        self.value_optimizer.zero_grad()
+        total_value_loss.backward()
+        self.value_optimizer.step()
+
+        ModelUtils.update_learning_rate(self.entropy_optimizer, decay_lr)
+        self.entropy_optimizer.zero_grad()
+        entropy_loss.backward()
+        self.entropy_optimizer.step()
+
+        # Update target network
+        self.soft_update(self.policy.actor_critic.critic, self.target_network, self.tau)
+        update_stats = {
+            "Losses/Policy Loss": abs(policy_loss.detach().cpu().numpy()),
+            "Losses/Value Loss": value_loss.detach().cpu().numpy(),
+            "Losses/Q1 Loss": q1_loss.detach().cpu().numpy(),
+            "Losses/Q2 Loss": q2_loss.detach().cpu().numpy(),
+            "Policy/Entropy Coeff": torch.exp(self._log_ent_coef)
+            .detach()
+            .cpu()
+            .numpy(),
+            "Policy/Learning Rate": decay_lr,
+        }
+
+        for signal in self.reward_signals.values():
+            signal.update(batch)
+
+        return update_stats
+
+    def update_reward_signals(
+        self, reward_signal_minibatches: Mapping[str, AgentBuffer], num_sequences: int
+    ) -> Dict[str, float]:
+        return {}

ml-agents/mlagents/trainers/tests/test_models.py

+import pytest
+
+from mlagents.trainers.tf.models import ModelUtils
+from mlagents.tf_utils import tf
+from mlagents_envs.base_env import BehaviorSpec, ActionType
+
+
+def create_behavior_spec(num_visual, num_vector, vector_size):
+    behavior_spec = BehaviorSpec(
+        [(84, 84, 3)] * int(num_visual) + [(vector_size,)] * int(num_vector),
+        ActionType.DISCRETE,
+        (1,),
+    )
+    return behavior_spec
+
+
+@pytest.mark.parametrize("num_visual", [1, 2, 4])
+@pytest.mark.parametrize("num_vector", [1, 2, 4])
+def test_create_input_placeholders(num_vector, num_visual):
+    vec_size = 8
+    name_prefix = "test123"
+    bspec = create_behavior_spec(num_visual, num_vector, vec_size)
+    vec_in, vis_in = ModelUtils.create_input_placeholders(
+        bspec.observation_shapes, name_prefix=name_prefix
+    )
+
+    assert isinstance(vis_in, list)
+    assert len(vis_in) == num_visual
+    assert isinstance(vec_in, tf.Tensor)
+    assert vec_in.get_shape().as_list()[1] == num_vector * 8
+
+    # Check names contain prefix and vis shapes are correct
+    for _vis in vis_in:
+        assert _vis.get_shape().as_list() == [None, 84, 84, 3]
+        assert _vis.name.startswith(name_prefix)
+    assert vec_in.name.startswith(name_prefix)

com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyPoseExtractor.cs.meta

+fileFormatVersion: 2
+guid: 11fe037a02b4a483cb9342c3454232cd
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodySensorComponent.cs

+#if UNITY_2020_1_OR_NEWER
+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    public class ArticulationBodySensorComponent  : SensorComponent
+    {
+        public ArticulationBody RootBody;
+
+        [SerializeField]
+        public PhysicsSensorSettings Settings = PhysicsSensorSettings.Default();
+        public string sensorName;
+
+        /// <summary>
+        /// Creates a PhysicsBodySensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor CreateSensor()
+        {
+            return new PhysicsBodySensor(RootBody, Settings, sensorName);
+        }
+
+        /// <inheritdoc/>
+        public override int[] GetObservationShape()
+        {
+            if (RootBody == null)
+            {
+                return new[] { 0 };
+            }
+
+            // TODO static method in PhysicsBodySensor?
+            // TODO only update PoseExtractor when body changes?
+            var poseExtractor = new ArticulationBodyPoseExtractor(RootBody);
+            var numPoseObservations = poseExtractor.GetNumPoseObservations(Settings);
+            var numJointObservations = 0;
+            // Start from i=1 to ignore the root
+            for (var i = 1; i < poseExtractor.Bodies.Length; i++)
+            {
+                numJointObservations += ArticulationBodyJointExtractor.NumObservations(
+                    poseExtractor.Bodies[i], Settings
+                );
+            }
+            return new[] { numPoseObservations + numJointObservations };
+        }
+    }
+
+}
+#endif // UNITY_2020_1_OR_NEWER

com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsSensorSettings.cs.meta

+fileFormatVersion: 2
+guid: fcb7a51f0d5f8404db7b85bd35ecc1fb
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+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

com.unity.ml-agents.extensions/Runtime/Sensors/ArticulationBodyPoseExtractor.cs

+#if UNITY_2020_1_OR_NEWER
+
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Utility class to track a hierarchy of ArticulationBodies.
+    /// </summary>
+    public class ArticulationBodyPoseExtractor : PoseExtractor
+    {
+        ArticulationBody[] m_Bodies;
+
+        public ArticulationBodyPoseExtractor(ArticulationBody rootBody)
+        {
+            if (rootBody == null)
+            {
+                return;
+            }
+
+            if (!rootBody.isRoot)
+            {
+                Debug.Log("Must pass ArticulationBody.isRoot");
+                return;
+            }
+
+            var bodies = rootBody.GetComponentsInChildren <ArticulationBody>();
+            if (bodies[0] != rootBody)
+            {
+                Debug.Log("Expected root body at index 0");
+                return;
+            }
+
+            var numBodies = bodies.Length;
+            m_Bodies = bodies;
+            int[] parentIndices = new int[numBodies];
+            parentIndices[0] = -1;
+
+            var bodyToIndex = new Dictionary<ArticulationBody, int>();
+            for (var i = 0; i < numBodies; i++)
+            {
+                bodyToIndex[m_Bodies[i]] = i;
+            }
+
+            for (var i = 1; i < numBodies; i++)
+            {
+                var currentArticBody = m_Bodies[i];
+                // Component.GetComponentInParent will consider the provided object as well.
+                // So start looking from the parent.
+                var currentGameObject = currentArticBody.gameObject;
+                var parentGameObject = currentGameObject.transform.parent;
+                var parentArticBody = parentGameObject.GetComponentInParent<ArticulationBody>();
+                parentIndices[i] = bodyToIndex[parentArticBody];
+            }
+
+            Setup(parentIndices);
+        }
+
+        /// <inheritdoc/>
+        protected internal override Vector3 GetLinearVelocityAt(int index)
+        {
+            return m_Bodies[index].velocity;
+        }
+
+        /// <inheritdoc/>
+        protected internal override Pose GetPoseAt(int index)
+        {
+            var body = m_Bodies[index];
+            var go = body.gameObject;
+            var t = go.transform;
+            return new Pose { rotation = t.rotation, position = t.position };
+        }
+
+        internal ArticulationBody[] Bodies => m_Bodies;
+    }
+}
+#endif // UNITY_2020_1_OR_NEWER

com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsBodySensor.cs

+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// ISensor implementation that generates observations for a group of Rigidbodies or ArticulationBodies.
+    /// </summary>
+    public class PhysicsBodySensor : ISensor
+    {
+        int[] m_Shape;
+        string m_SensorName;
+
+        PoseExtractor m_PoseExtractor;
+        IJointExtractor[] m_JointExtractors;
+        PhysicsSensorSettings m_Settings;
+
+        /// <summary>
+        ///  Construct a new PhysicsBodySensor
+        /// </summary>
+        /// <param name="rootBody">The root Rigidbody. This has no Joints on it (but other Joints may connect to it).</param>
+        /// <param name="rootGameObject">Optional GameObject used to find Rigidbodies in the hierarchy.</param>
+        /// <param name="virtualRoot">Optional GameObject used to determine the root of the poses,
+        /// <param name="settings"></param>
+        /// <param name="sensorName"></param>
+        public PhysicsBodySensor(
+            Rigidbody rootBody,
+            GameObject rootGameObject,
+            GameObject virtualRoot,
+            PhysicsSensorSettings settings,
+            string sensorName=null
+        )
+        {
+            var poseExtractor = new RigidBodyPoseExtractor(rootBody, rootGameObject, virtualRoot);
+            m_PoseExtractor = poseExtractor;
+            m_SensorName = string.IsNullOrEmpty(sensorName) ? $"PhysicsBodySensor:{rootBody?.name}" : sensorName;
+            m_Settings = settings;
+
+            var numJointExtractorObservations = 0;
+            var rigidBodies = poseExtractor.Bodies;
+            if (rigidBodies != null)
+            {
+                m_JointExtractors = new IJointExtractor[rigidBodies.Length - 1]; // skip the root
+                for (var i = 1; i < rigidBodies.Length; i++)
+                {
+                    var jointExtractor = new RigidBodyJointExtractor(rigidBodies[i]);
+                    numJointExtractorObservations += jointExtractor.NumObservations(settings);
+                    m_JointExtractors[i - 1] = jointExtractor;
+                }
+            }
+            else
+            {
+                m_JointExtractors = new IJointExtractor[0];
+            }
+
+            var numTransformObservations = m_PoseExtractor.GetNumPoseObservations(settings);
+            m_Shape = new[] { numTransformObservations + numJointExtractorObservations };
+        }
+
+#if UNITY_2020_1_OR_NEWER
+        public PhysicsBodySensor(ArticulationBody rootBody, PhysicsSensorSettings settings, string sensorName=null)
+        {
+            var poseExtractor = new ArticulationBodyPoseExtractor(rootBody);
+            m_PoseExtractor = poseExtractor;
+            m_SensorName = string.IsNullOrEmpty(sensorName) ? $"ArticulationBodySensor:{rootBody?.name}" : sensorName;
+            m_Settings = settings;
+
+            var numJointExtractorObservations = 0;
+            var articBodies = poseExtractor.Bodies;
+            if (articBodies != null)
+            {
+                m_JointExtractors = new IJointExtractor[articBodies.Length - 1]; // skip the root
+                for (var i = 1; i < articBodies.Length; i++)
+                {
+                    var jointExtractor = new ArticulationBodyJointExtractor(articBodies[i]);
+                    numJointExtractorObservations += jointExtractor.NumObservations(settings);
+                    m_JointExtractors[i - 1] = jointExtractor;
+                }
+            }
+            else
+            {
+                m_JointExtractors = new IJointExtractor[0];
+            }
+
+            var numTransformObservations = m_PoseExtractor.GetNumPoseObservations(settings);
+            m_Shape = new[] { numTransformObservations + numJointExtractorObservations };
+        }
+#endif
+
+        /// <inheritdoc/>
+        public int[] GetObservationShape()
+        {
+            return m_Shape;
+        }
+
+        /// <inheritdoc/>
+        public int Write(ObservationWriter writer)
+        {
+            var numWritten = writer.WritePoses(m_Settings, m_PoseExtractor);
+            foreach (var jointExtractor in m_JointExtractors)
+            {
+                numWritten += jointExtractor.Write(m_Settings, writer, numWritten);
+            }
+            return numWritten;
+        }
+
+        /// <inheritdoc/>
+        public byte[] GetCompressedObservation()
+        {
+            return null;
+        }
+
+        /// <inheritdoc/>
+        public void Update()
+        {
+            if (m_Settings.UseModelSpace)
+            {
+                m_PoseExtractor.UpdateModelSpacePoses();
+            }
+
+            if (m_Settings.UseLocalSpace)
+            {
+                m_PoseExtractor.UpdateLocalSpacePoses();
+            }
+        }
+
+        /// <inheritdoc/>
+        public void Reset() {}
+
+        /// <inheritdoc/>
+        public SensorCompressionType GetCompressionType()
+        {
+            return SensorCompressionType.None;
+        }
+
+        /// <inheritdoc/>
+        public string GetName()
+        {
+            return m_SensorName;
+        }
+    }
+}

com.unity.ml-agents.extensions/Runtime/Sensors/PhysicsSensorSettings.cs

+using System;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Settings that define the observations generated for physics-based sensors.
+    /// </summary>
+    [Serializable]
+    public struct PhysicsSensorSettings
+    {
+        /// <summary>
+        /// Whether to use model space (relative to the root body) translations as observations.
+        /// </summary>
+        public bool UseModelSpaceTranslations;
+
+        /// <summary>
+        /// Whether to use model space (relative to the root body) rotations as observations.
+        /// </summary>
+        public bool UseModelSpaceRotations;
+
+        /// <summary>
+        /// Whether to use local space (relative to the parent body) translations as observations.
+        /// </summary>
+        public bool UseLocalSpaceTranslations;
+
+        /// <summary>
+        /// Whether to use local space (relative to the parent body) translations as observations.
+        /// </summary>
+        public bool UseLocalSpaceRotations;
+
+        /// <summary>
+        /// Whether to use model space (relative to the root body) linear velocities as observations.
+        /// </summary>
+        public bool UseModelSpaceLinearVelocity;
+
+        /// <summary>
+        /// Whether to use local space (relative to the parent body) linear velocities as observations.
+        /// </summary>
+        public bool UseLocalSpaceLinearVelocity;
+
+        /// <summary>
+        /// Whether to use joint-specific positions and angles as observations.
+        /// </summary>
+        public bool UseJointPositionsAndAngles;
+
+        /// <summary>
+        /// Whether to use the joint forces and torques that are applied by the solver as observations.
+        /// </summary>
+        public bool UseJointForces;
+
+        /// <summary>
+        /// Creates a PhysicsSensorSettings with reasonable default values.
+        /// </summary>
+        /// <returns></returns>
+        public static PhysicsSensorSettings Default()
+        {
+            return new PhysicsSensorSettings
+            {
+                UseModelSpaceTranslations = true,
+                UseModelSpaceRotations = true,
+            };
+        }
+
+        /// <summary>
+        /// Whether any model space observations are being used.
+        /// </summary>
+        public bool UseModelSpace
+        {
+            get { return UseModelSpaceTranslations || UseModelSpaceRotations || UseModelSpaceLinearVelocity; }
+        }
+
+        /// <summary>
+        /// Whether any local space observations are being used.
+        /// </summary>
+        public bool UseLocalSpace
+        {
+            get { return UseLocalSpaceTranslations || UseLocalSpaceRotations || UseLocalSpaceLinearVelocity; }
+        }
+    }
+
+    internal static class ObservationWriterPhysicsExtensions
+    {
+        /// <summary>
+        /// Utility method for writing a PoseExtractor to an ObservationWriter.
+        /// </summary>
+        /// <param name="writer"></param>
+        /// <param name="settings"></param>
+        /// <param name="poseExtractor"></param>
+        /// <param name="baseOffset">The offset into the ObservationWriter to start writing at.</param>
+        /// <returns>The number of observations written.</returns>
+        public static int WritePoses(this ObservationWriter writer, PhysicsSensorSettings settings, PoseExtractor poseExtractor, int baseOffset = 0)
+        {
+            var offset = baseOffset;
+            if (settings.UseModelSpace)
+            {
+                foreach (var pose in poseExtractor.GetEnabledModelSpacePoses())
+                {
+                    if (settings.UseModelSpaceTranslations)
+                    {
+                        writer.Add(pose.position, offset);
+                        offset += 3;
+                    }
+
+                    if (settings.UseModelSpaceRotations)
+                    {
+                        writer.Add(pose.rotation, offset);
+                        offset += 4;
+                    }
+                }
+
+                foreach(var vel in poseExtractor.GetEnabledModelSpaceVelocities())
+                {
+                    if (settings.UseModelSpaceLinearVelocity)
+                    {
+                        writer.Add(vel, offset);
+                        offset += 3;
+                    }
+                }
+            }
+
+            if (settings.UseLocalSpace)
+            {
+                foreach (var pose in poseExtractor.GetEnabledLocalSpacePoses())
+                {
+                    if (settings.UseLocalSpaceTranslations)
+                    {
+                        writer.Add(pose.position, offset);
+                        offset += 3;
+                    }
+
+                    if (settings.UseLocalSpaceRotations)
+                    {
+                        writer.Add(pose.rotation, offset);
+                        offset += 4;
+                    }
+                }
+
+                foreach(var vel in poseExtractor.GetEnabledLocalSpaceVelocities())
+                {
+                    if (settings.UseLocalSpaceLinearVelocity)
+                    {
+                        writer.Add(vel, offset);
+                        offset += 3;
+                    }
+                }
+            }
+
+            return offset - baseOffset;
+        }
+    }
+}

com.unity.ml-agents.extensions/Runtime/Sensors/PoseExtractor.cs

+using System.Collections.Generic;
+using UnityEngine;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Abstract class for managing the transforms of a hierarchy of objects.
+    /// This could be GameObjects or Monobehaviours in the scene graph, but this is
+    /// not a requirement; for example, the objects could be rigid bodies whose hierarchy
+    /// is defined by Joint configurations.
+    ///
+    /// Poses are either considered in model space, which is relative to a root body,
+    /// or in local space, which is relative to their parent.
+    /// </summary>
+    public abstract class PoseExtractor
+    {
+        int[] m_ParentIndices;
+        Pose[] m_ModelSpacePoses;
+        Pose[] m_LocalSpacePoses;
+
+        Vector3[] m_ModelSpaceLinearVelocities;
+        Vector3[] m_LocalSpaceLinearVelocities;
+
+        bool[] m_PoseEnabled;
+
+
+        /// <summary>
+        /// Read iterator for the enabled model space transforms.
+        /// </summary>
+        public IEnumerable<Pose> GetEnabledModelSpacePoses()
+        {
+            if (m_ModelSpacePoses == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_ModelSpacePoses.Length; i++)
+            {
+                if (m_PoseEnabled[i])
+                {
+                    yield return m_ModelSpacePoses[i];
+                }
+            }
+        }
+
+        /// <summary>
+        /// Read iterator for the enabled local space transforms.
+        /// </summary>
+        public IEnumerable<Pose> GetEnabledLocalSpacePoses()
+        {
+            if (m_LocalSpacePoses == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_LocalSpacePoses.Length; i++)
+            {
+                if (m_PoseEnabled[i])
+                {
+                    yield return m_LocalSpacePoses[i];
+                }
+            }
+        }
+
+        /// <summary>
+        /// Read iterator for the enabled model space linear velocities.
+        /// </summary>
+        public IEnumerable<Vector3> GetEnabledModelSpaceVelocities()
+        {
+            if (m_ModelSpaceLinearVelocities == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_ModelSpaceLinearVelocities.Length; i++)
+            {
+                if (m_PoseEnabled[i])
+                {
+                    yield return m_ModelSpaceLinearVelocities[i];
+                }
+            }
+        }
+
+        /// <summary>
+        /// Read iterator for the enabled local space linear velocities.
+        /// </summary>
+        public IEnumerable<Vector3> GetEnabledLocalSpaceVelocities()
+        {
+            if (m_LocalSpaceLinearVelocities == null)
+            {
+                yield break;
+            }
+
+            for (var i = 0; i < m_LocalSpaceLinearVelocities.Length; i++)
+            {
+                if (m_PoseEnabled[i])
+                {
+                    yield return m_LocalSpaceLinearVelocities[i];
+                }
+            }
+        }
+
+        /// <summary>
+        /// Number of enabled poses in the hierarchy (read-only).
+        /// </summary>
+        public int NumEnabledPoses
+        {
+            get
+            {
+                if (m_PoseEnabled == null)
+                {
+                    return 0;
+                }
+
+                var numEnabled = 0;
+                for (var i = 0; i < m_PoseEnabled.Length; i++)
+                {
+                    numEnabled += m_PoseEnabled[i] ? 1 : 0;
+                }
+
+                return numEnabled;
+            }
+        }
+
+        /// <summary>
+        /// Number of total poses in the hierarchy (read-only).
+        /// </summary>
+        public int NumPoses
+        {
+            get { return m_ModelSpacePoses?.Length ?? 0; }
+        }
+
+        /// <summary>
+        /// Get the parent index of the body at the specified index.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <returns></returns>
+        public int GetParentIndex(int index)
+        {
+            if (m_ParentIndices == null)
+            {
+                return -1;
+            }
+
+            return m_ParentIndices[index];
+        }
+
+        /// <summary>
+        /// Set whether the pose at the given index is enabled or disabled for observations.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <param name="val"></param>
+        public void SetPoseEnabled(int index, bool val)
+        {
+            m_PoseEnabled[index] = val;
+        }
+
+        /// <summary>
+        /// Initialize with the mapping of parent indices.
+        /// The 0th element is assumed to be -1, indicating that it's the root.
+        /// </summary>
+        /// <param name="parentIndices"></param>
+        protected void Setup(int[] parentIndices)
+        {
+#if DEBUG
+            if (parentIndices[0] != -1)
+            {
+                throw new UnityAgentsException($"Expected parentIndices[0] to be -1, got {parentIndices[0]}");
+            }
+#endif
+            m_ParentIndices = parentIndices;
+            var numPoses = parentIndices.Length;
+            m_ModelSpacePoses = new Pose[numPoses];
+            m_LocalSpacePoses = new Pose[numPoses];
+
+            m_ModelSpaceLinearVelocities = new Vector3[numPoses];
+            m_LocalSpaceLinearVelocities = new Vector3[numPoses];
+
+            m_PoseEnabled = new bool[numPoses];
+            // All poses are enabled by default. Generally we'll want to disable the root though.
+            for (var i = 0; i < numPoses; i++)
+            {
+                m_PoseEnabled[i] = true;
+            }
+        }
+
+        /// <summary>
+        /// Return the world space Pose of the i'th object.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <returns></returns>
+        protected internal abstract Pose GetPoseAt(int index);
+
+        /// <summary>
+        /// Return the world space linear velocity of the i'th object.
+        /// </summary>
+        /// <param name="index"></param>
+        /// <returns></returns>
+        protected internal abstract Vector3 GetLinearVelocityAt(int index);
+
+
+        /// <summary>
+        /// Update the internal model space transform storage based on the underlying system.
+        /// </summary>
+        public void UpdateModelSpacePoses()
+        {
+            using (TimerStack.Instance.Scoped("UpdateModelSpacePoses"))
+            {
+                if (m_ModelSpacePoses == null)
+                {
+                    return;
+                }
+
+                var rootWorldTransform = GetPoseAt(0);
+                var worldToModel = rootWorldTransform.Inverse();
+                var rootLinearVel = GetLinearVelocityAt(0);
+
+                for (var i = 0; i < m_ModelSpacePoses.Length; i++)
+                {
+                    var currentWorldSpacePose = GetPoseAt(i);
+                    var currentModelSpacePose = worldToModel.Multiply(currentWorldSpacePose);
+                    m_ModelSpacePoses[i] = currentModelSpacePose;
+
+                    var currentBodyLinearVel = GetLinearVelocityAt(i);
+                    var relativeVelocity = currentBodyLinearVel - rootLinearVel;
+                    m_ModelSpaceLinearVelocities[i] = worldToModel.rotation * relativeVelocity;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Update the internal model space transform storage based on the underlying system.
+        /// </summary>
+        public void UpdateLocalSpacePoses()
+        {
+            using (TimerStack.Instance.Scoped("UpdateLocalSpacePoses"))
+            {
+                if (m_LocalSpacePoses == null)
+                {
+                    return;
+                }
+
+                for (var i = 0; i < m_LocalSpacePoses.Length; i++)
+                {
+                    if (m_ParentIndices[i] != -1)
+                    {
+                        var parentTransform = GetPoseAt(m_ParentIndices[i]);
+                        // This is slightly inefficient, since for a body with multiple children, we'll end up inverting
+                        // the transform multiple times. Might be able to trade space for perf here.
+                        var invParent = parentTransform.Inverse();
+                        var currentTransform = GetPoseAt(i);
+                        m_LocalSpacePoses[i] = invParent.Multiply(currentTransform);
+
+                        var parentLinearVel = GetLinearVelocityAt(m_ParentIndices[i]);
+                        var currentLinearVel = GetLinearVelocityAt(i);
+                        m_LocalSpaceLinearVelocities[i] = invParent.rotation * (currentLinearVel - parentLinearVel);
+                    }
+                    else
+                    {
+                        m_LocalSpacePoses[i] = Pose.identity;
+                        m_LocalSpaceLinearVelocities[i] = Vector3.zero;
+                    }
+                }
+            }
+        }
+
+        /// <summary>
+        /// Compute the number of floats needed to represent the poses for the given PhysicsSensorSettings.
+        /// </summary>
+        /// <param name="settings"></param>
+        /// <returns></returns>
+        public int GetNumPoseObservations(PhysicsSensorSettings settings)
+        {
+            int obsPerPose = 0;
+            obsPerPose += settings.UseModelSpaceTranslations ? 3 : 0;
+            obsPerPose += settings.UseModelSpaceRotations ? 4 : 0;
+            obsPerPose += settings.UseLocalSpaceTranslations ? 3 : 0;
+            obsPerPose += settings.UseLocalSpaceRotations ? 4 : 0;
+
+            obsPerPose += settings.UseModelSpaceLinearVelocity ? 3 : 0;
+            obsPerPose += settings.UseLocalSpaceLinearVelocity ? 3 : 0;
+
+            return NumEnabledPoses * obsPerPose;
+        }
+
+        internal void DrawModelSpace(Vector3 offset)
+        {
+            UpdateLocalSpacePoses();
+            UpdateModelSpacePoses();
+
+            var pose = m_ModelSpacePoses;
+            var localPose = m_LocalSpacePoses;
+            for (var i = 0; i < pose.Length; i++)
+            {
+                var current = pose[i];
+                if (m_ParentIndices[i] == -1)
+                {
+                    continue;
+                }
+
+                var parent = pose[m_ParentIndices[i]];
+                Debug.DrawLine(current.position + offset, parent.position + offset, Color.cyan);
+                var localUp = localPose[i].rotation * Vector3.up;
+                var localFwd = localPose[i].rotation * Vector3.forward;
+                var localRight = localPose[i].rotation * Vector3.right;
+                Debug.DrawLine(current.position+offset, current.position+offset+.1f*localUp, Color.red);
+                Debug.DrawLine(current.position+offset, current.position+offset+.1f*localFwd, Color.green);
+                Debug.DrawLine(current.position+offset, current.position+offset+.1f*localRight, Color.blue);
+            }
+        }
+    }
+
+    /// <summary>
+    /// Extension methods for the Pose struct, in order to improve the readability of some math.
+    /// </summary>
+    public static class PoseExtensions
+    {
+        /// <summary>
+        /// Compute the inverse of a Pose. For any Pose P,
+        ///   P.Inverse() * P
+        /// will equal the identity pose (within tolerance).
+        /// </summary>
+        /// <param name="pose"></param>
+        /// <returns></returns>
+        public static Pose Inverse(this Pose pose)
+        {
+            var rotationInverse = Quaternion.Inverse(pose.rotation);
+            var translationInverse = -(rotationInverse * pose.position);
+            return new Pose { rotation = rotationInverse, position = translationInverse };
+        }
+
+        /// <summary>
+        /// This is equivalent to Pose.GetTransformedBy(), but keeps the order more intuitive.
+        /// </summary>
+        /// <param name="pose"></param>
+        /// <param name="rhs"></param>
+        /// <returns></returns>
+        public static Pose Multiply(this Pose pose, Pose rhs)
+        {
+            return rhs.GetTransformedBy(pose);
+        }
+
+        /// <summary>
+        /// Transform the vector by the pose. Conceptually this is equivalent to treating the Pose
+        /// as a 4x4 matrix and multiplying the augmented vector.
+        /// See https://en.wikipedia.org/wiki/Affine_transformation#Augmented_matrix for more details.
+        /// </summary>
+        /// <param name="pose"></param>
+        /// <param name="rhs"></param>
+        /// <returns></returns>
+        public static Vector3 Multiply(this Pose pose, Vector3 rhs)
+        {
+            return pose.rotation * rhs + pose.position;
+        }
+
+        // TODO optimize inv(A)*B?
+    }
+}

com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodyPoseExtractor.cs

+using System.Collections.Generic;
+using UnityEngine;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+
+    /// <summary>
+    /// Utility class to track a hierarchy of RigidBodies. These are assumed to have a root node,
+    /// and child nodes are connect to their parents via Joints.
+    /// </summary>
+    public class RigidBodyPoseExtractor : PoseExtractor
+    {
+        Rigidbody[] m_Bodies;
+
+        /// <summary>
+        /// Optional game object used to determine the root of the poses, separate from the actual Rigidbodies
+        /// in the hierarchy. For locomotion
+        /// </summary>
+        GameObject m_VirtualRoot;
+
+        /// <summary>
+        /// Initialize given a root RigidBody.
+        /// </summary>
+        /// <param name="rootBody">The root Rigidbody. This has no Joints on it (but other Joints may connect to it).</param>
+        /// <param name="rootGameObject">Optional GameObject used to find Rigidbodies in the hierarchy.</param>
+        /// <param name="virtualRoot">Optional GameObject used to determine the root of the poses,
+        /// separate from the actual Rigidbodies in the hierarchy. For locomotion tasks, with ragdolls, this provides
+        /// a stabilized refernece frame, which can improve learning.</param>
+        public RigidBodyPoseExtractor(Rigidbody rootBody, GameObject rootGameObject = null, GameObject virtualRoot = null)
+        {
+            if (rootBody == null)
+            {
+                return;
+            }
+
+            Rigidbody[] rbs;
+            if (rootGameObject == null)
+            {
+                rbs = rootBody.GetComponentsInChildren<Rigidbody>();
+            }
+            else
+            {
+                rbs = rootGameObject.GetComponentsInChildren<Rigidbody>();
+            }
+
+            if (rbs == null || rbs.Length == 0)
+            {
+                Debug.Log("No rigid bodies found!");
+                return;
+            }
+
+                if (rbs[0] != rootBody)
+            {
+                Debug.Log("Expected root body at index 0");
+                return;
+            }
+
+            // Adjust the array if we have a virtual root.
+            // This will be at index 0, and the "real" root will be parented to it.
+            if (virtualRoot != null)
+            {
+                var extendedRbs = new Rigidbody[rbs.Length + 1];
+                for (var i = 0; i < rbs.Length; i++)
+                {
+                    extendedRbs[i + 1] = rbs[i];
+                }
+
+                rbs = extendedRbs;
+            }
+
+            var bodyToIndex = new Dictionary<Rigidbody, int>(rbs.Length);
+            var parentIndices = new int[rbs.Length];
+            parentIndices[0] = -1;
+
+            for (var i = 0; i < rbs.Length; i++)
+            {
+                if(rbs[i] != null)
+                {
+                    bodyToIndex[rbs[i]] = i;
+                }
+            }
+
+            var joints = rootBody.GetComponentsInChildren <Joint>();
+
+
+            foreach (var j in joints)
+            {
+                var parent = j.connectedBody;
+                var child = j.GetComponent<Rigidbody>();
+
+                var parentIndex = bodyToIndex[parent];
+                var childIndex = bodyToIndex[child];
+                parentIndices[childIndex] = parentIndex;
+            }
+
+            if (virtualRoot != null)
+            {
+                // Make sure the original root treats the virtual root as its parent.
+                parentIndices[1] = 0;
+                m_VirtualRoot = virtualRoot;
+            }
+
+            m_Bodies = rbs;
+            Setup(parentIndices);
+
+            // By default, ignore the root
+            SetPoseEnabled(0, false);
+        }
+
+        /// <inheritdoc/>
+        protected internal override Vector3 GetLinearVelocityAt(int index)
+        {
+            if (index == 0 && m_VirtualRoot != null)
+            {
+                // No velocity on the virtual root
+                return Vector3.zero;
+            }
+            return m_Bodies[index].velocity;
+        }
+
+        /// <inheritdoc/>
+        protected internal override Pose GetPoseAt(int index)
+        {
+            if (index == 0 && m_VirtualRoot != null)
+            {
+                // Use the GameObject's world transform
+                return new Pose
+                {
+                    rotation = m_VirtualRoot.transform.rotation,
+                    position = m_VirtualRoot.transform.position
+                };
+            }
+
+            var body = m_Bodies[index];
+            return new Pose { rotation = body.rotation, position = body.position };
+        }
+
+        internal Rigidbody[] Bodies => m_Bodies;
+    }
+
+}

com.unity.ml-agents.extensions/Runtime/Sensors/RigidBodySensorComponent.cs

+using UnityEngine;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Sensors
+{
+    /// <summary>
+    /// Editor component that creates a PhysicsBodySensor for the Agent.
+    /// </summary>
+    public class RigidBodySensorComponent  : SensorComponent
+    {
+        /// <summary>
+        /// The root Rigidbody of the system.
+        /// </summary>
+        public Rigidbody RootBody;
+
+        /// <summary>
+        /// Optional GameObject used to determine the root of the poses.
+        /// </summary>
+        public GameObject VirtualRoot;
+
+        /// <summary>
+        /// Settings defining what types of observations will be generated.
+        /// </summary>
+        [SerializeField]
+        public PhysicsSensorSettings Settings = PhysicsSensorSettings.Default();
+
+        /// <summary>
+        /// Optional sensor name. This must be unique for each Agent.
+        /// </summary>
+        public string sensorName;
+
+        /// <summary>
+        /// Creates a PhysicsBodySensor.
+        /// </summary>
+        /// <returns></returns>
+        public override ISensor CreateSensor()
+        {
+            return new PhysicsBodySensor(RootBody, gameObject, VirtualRoot, Settings, sensorName);
+        }
+
+        /// <inheritdoc/>
+        public override int[] GetObservationShape()
+        {
+            if (RootBody == null)
+            {
+                return new[] { 0 };
+            }
+
+            // TODO static method in PhysicsBodySensor?
+            // TODO only update PoseExtractor when body changes?
+            var poseExtractor = new RigidBodyPoseExtractor(RootBody, gameObject, VirtualRoot);
+            var numPoseObservations = poseExtractor.GetNumPoseObservations(Settings);
+
+            var numJointObservations = 0;
+            // Start from i=1 to ignore the root
+            for (var i = 1; i < poseExtractor.Bodies.Length; i++)
+            {
+                var body = poseExtractor.Bodies[i];
+                var joint = body?.GetComponent<Joint>();
+                numJointObservations += RigidBodyJointExtractor.NumObservations(body, joint, Settings);
+            }
+            return new[] { numPoseObservations + numJointObservations };
+        }
+    }
+
+}

com.unity.ml-agents.extensions/Tests/Editor/Sensors/ArticulationBodySensorTests.cs

+#if UNITY_2020_1_OR_NEWER
+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Extensions.Sensors;
+
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+
+    public class ArticulationBodySensorTests
+    {
+        [Test]
+        public void TestNullRootBody()
+        {
+            var gameObj = new GameObject();
+
+            var sensorComponent = gameObj.AddComponent<ArticulationBodySensorComponent>();
+            var sensor = sensorComponent.CreateSensor();
+            SensorTestHelper.CompareObservation(sensor, new float[0]);
+        }
+
+        [Test]
+        public void TestSingleBody()
+        {
+            var gameObj = new GameObject();
+            var articulationBody = gameObj.AddComponent<ArticulationBody>();
+            var sensorComponent = gameObj.AddComponent<ArticulationBodySensorComponent>();
+            sensorComponent.RootBody = articulationBody;
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseModelSpaceLinearVelocity = true,
+                UseLocalSpaceTranslations = true,
+                UseLocalSpaceRotations = true
+            };
+
+            var sensor = sensorComponent.CreateSensor();
+            sensor.Update();
+            var expected = new[]
+            {
+                0f, 0f, 0f, // ModelSpaceLinearVelocity
+                0f, 0f, 0f, // LocalSpaceTranslations
+                0f, 0f, 0f, 1f // LocalSpaceRotations
+            };
+            SensorTestHelper.CompareObservation(sensor, expected);
+            Assert.AreEqual(expected.Length, sensorComponent.GetObservationShape()[0]);
+        }
+
+        [Test]
+        public void TestBodiesWithJoint()
+        {
+            var rootObj = new GameObject();
+            var rootArticBody = rootObj.AddComponent<ArticulationBody>();
+
+            var middleGamObj = new GameObject();
+            var middleArticBody = middleGamObj.AddComponent<ArticulationBody>();
+            middleArticBody.AddForce(new Vector3(0f, 1f, 0f));
+            middleGamObj.transform.SetParent(rootObj.transform);
+            middleGamObj.transform.localPosition = new Vector3(13.37f, 0f, 0f);
+            middleArticBody.jointType = ArticulationJointType.RevoluteJoint;
+
+            var leafGameObj = new GameObject();
+            var leafArticBody = leafGameObj.AddComponent<ArticulationBody>();
+            leafGameObj.transform.SetParent(middleGamObj.transform);
+            leafGameObj.transform.localPosition = new Vector3(4.2f, 0f, 0f);
+            leafArticBody.jointType = ArticulationJointType.PrismaticJoint;
+            leafArticBody.linearLockZ = ArticulationDofLock.LimitedMotion;
+            leafArticBody.zDrive = new ArticulationDrive
+            {
+                lowerLimit = -3,
+                upperLimit = 1
+            };
+
+
+#if UNITY_2020_2_OR_NEWER
+            // ArticulationBody.velocity is read-only in 2020.1
+            rootArticBody.velocity = new Vector3(1f, 0f, 0f);
+            middleArticBody.velocity = new Vector3(0f, 1f, 0f);
+            leafArticBody.velocity = new Vector3(0f, 0f, 1f);
+#endif
+
+            var sensorComponent = rootObj.AddComponent<ArticulationBodySensorComponent>();
+            sensorComponent.RootBody = rootArticBody;
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseModelSpaceTranslations = true,
+                UseLocalSpaceTranslations = true,
+#if UNITY_2020_2_OR_NEWER
+                UseLocalSpaceLinearVelocity = true
+#endif
+            };
+
+            var sensor = sensorComponent.CreateSensor();
+            sensor.Update();
+            var expected = new[]
+            {
+                // Model space
+                0f, 0f, 0f, // Root pos
+                13.37f, 0f, 0f, // Middle pos
+                leafGameObj.transform.position.x, 0f, 0f, // Leaf pos
+
+                // Local space
+                0f, 0f, 0f, // Root pos
+                13.37f, 0f, 0f, // Attached pos
+                4.2f, 0f, 0f, // Leaf pos
+
+#if UNITY_2020_2_OR_NEWER
+                0f, 0f, 0f, // Root vel
+                -1f, 1f, 0f, // Attached vel
+                0f, -1f, 1f // Leaf vel
+#endif
+            };
+            SensorTestHelper.CompareObservation(sensor, expected);
+            Assert.AreEqual(expected.Length, sensorComponent.GetObservationShape()[0]);
+
+            // Update the settings to only process joint observations
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseJointForces = true,
+                UseJointPositionsAndAngles = true,
+            };
+
+            sensor = sensorComponent.CreateSensor();
+            sensor.Update();
+
+            expected = new[]
+            {
+                // revolute
+                0f, 1f, // joint1.position (sin and cos)
+                0f, // joint1.force
+
+                // prismatic
+                0.5f, // joint2.position (interpolate between limits)
+                0f, // joint2.force
+            };
+            SensorTestHelper.CompareObservation(sensor, expected);
+            Assert.AreEqual(expected.Length, sensorComponent.GetObservationShape()[0]);
+        }
+    }
+}
+#endif // #if UNITY_2020_1_OR_NEWER

com.unity.ml-agents.extensions/Tests/Editor/Sensors/PoseExtractorTests.cs

+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Extensions.Sensors;
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+    public class PoseExtractorTests
+    {
+        class UselessPoseExtractor : PoseExtractor
+        {
+            protected internal override Pose GetPoseAt(int index)
+            {
+                return Pose.identity;
+            }
+
+            protected internal override Vector3 GetLinearVelocityAt(int index)
+            {
+                return Vector3.zero;
+            }
+
+            public void Init(int[] parentIndices)
+            {
+                Setup(parentIndices);
+            }
+        }
+
+        [Test]
+        public void TestEmptyExtractor()
+        {
+            var poseExtractor = new UselessPoseExtractor();
+
+            // These should be no-ops
+            poseExtractor.UpdateLocalSpacePoses();
+            poseExtractor.UpdateModelSpacePoses();
+
+            Assert.AreEqual(0, poseExtractor.NumPoses);
+        }
+
+        [Test]
+        public void TestSimpleExtractor()
+        {
+            var poseExtractor = new UselessPoseExtractor();
+            var parentIndices = new[] { -1, 0 };
+            poseExtractor.Init(parentIndices);
+            Assert.AreEqual(2, poseExtractor.NumPoses);
+        }
+
+
+        /// <summary>
+        /// A simple "chain" hierarchy, where each object is parented to the one before it.
+        ///   0 <- 1 <- 2 <- ...
+        /// </summary>
+        class ChainPoseExtractor : PoseExtractor
+        {
+            public Vector3 offset;
+            public ChainPoseExtractor(int size)
+            {
+                var parents = new int[size];
+                for (var i = 0; i < size; i++)
+                {
+                    parents[i] = i - 1;
+                }
+                Setup(parents);
+            }
+
+            protected internal override Pose GetPoseAt(int index)
+            {
+                var rotation = Quaternion.identity;
+                var translation = offset + new Vector3(index, index, index);
+                return new Pose
+                {
+                    rotation = rotation,
+                    position = translation
+                };
+            }
+
+            protected  internal override Vector3 GetLinearVelocityAt(int index)
+            {
+                return Vector3.zero;
+            }
+
+        }
+
+        [Test]
+        public void TestChain()
+        {
+            var size = 4;
+            var chain = new ChainPoseExtractor(size);
+            chain.offset = new Vector3(.5f, .75f, .333f);
+
+            chain.UpdateModelSpacePoses();
+            chain.UpdateLocalSpacePoses();
+
+
+            var modelPoseIndex = 0;
+            foreach (var modelSpace in chain.GetEnabledModelSpacePoses())
+            {
+                if (modelPoseIndex == 0)
+                {
+                    // Root transforms are currently always the identity.
+                    Assert.IsTrue(modelSpace == Pose.identity);
+                }
+                else
+                {
+                    var expectedModelTranslation = new Vector3(modelPoseIndex, modelPoseIndex, modelPoseIndex);
+                    Assert.IsTrue(expectedModelTranslation == modelSpace.position);
+
+                }
+                modelPoseIndex++;
+            }
+            Assert.AreEqual(size, modelPoseIndex);
+
+            var localPoseIndex = 0;
+            foreach (var localSpace in chain.GetEnabledLocalSpacePoses())
+            {
+                if (localPoseIndex == 0)
+                {
+                    // Root transforms are currently always the identity.
+                    Assert.IsTrue(localSpace == Pose.identity);
+                }
+                else
+                {
+                    var expectedLocalTranslation = new Vector3(1, 1, 1);
+                    Assert.IsTrue(expectedLocalTranslation == localSpace.position, $"{expectedLocalTranslation} != {localSpace.position}");
+                }
+
+                localPoseIndex++;
+            }
+            Assert.AreEqual(size, localPoseIndex);
+        }
+
+        class BadPoseExtractor : PoseExtractor
+        {
+            public BadPoseExtractor()
+            {
+                var size = 2;
+                var parents = new int[size];
+                // Parents are intentionally invalid - expect -1 at root
+                for (var i = 0; i < size; i++)
+                {
+                    parents[i] = i;
+                }
+                Setup(parents);
+            }
+
+            protected internal override Pose GetPoseAt(int index)
+            {
+                return Pose.identity;
+            }
+
+            protected  internal override Vector3 GetLinearVelocityAt(int index)
+            {
+                return Vector3.zero;
+            }
+        }
+
+        [Test]
+        public void TestExpectedRoot()
+        {
+            Assert.Throws<UnityAgentsException>(() =>
+            {
+                var bad = new BadPoseExtractor();
+            });
+        }
+    }
+
+    public class PoseExtensionTests
+    {
+        [Test]
+        public void TestInverse()
+        {
+            Pose t = new Pose
+            {
+                rotation = Quaternion.AngleAxis(23.0f, new Vector3(1, 1, 1).normalized),
+                position = new Vector3(-1.0f, 2.0f, 3.0f)
+            };
+
+            var inverseT = t.Inverse();
+            var product = inverseT.Multiply(t);
+            Assert.IsTrue(Vector3.zero == product.position);
+            Assert.IsTrue(Quaternion.identity == product.rotation);
+
+            Assert.IsTrue(Pose.identity == product);
+        }
+
+    }
+}

com.unity.ml-agents.extensions/Tests/Editor/Sensors/RigidBodyPoseExtractorTests.cs

+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Extensions.Sensors;
+using UnityEditor;
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+    public class RigidBodyPoseExtractorTests
+    {
+        [TearDown]
+        public void RemoveGameObjects()
+        {
+            var objects = GameObject.FindObjectsOfType<GameObject>();
+            foreach (var o in objects)
+            {
+                UnityEngine.Object.DestroyImmediate(o);
+            }
+        }
+
+        [Test]
+        public void TestNullRoot()
+        {
+            var poseExtractor = new RigidBodyPoseExtractor(null);
+            // These should be no-ops
+            poseExtractor.UpdateLocalSpacePoses();
+            poseExtractor.UpdateModelSpacePoses();
+
+            Assert.AreEqual(0, poseExtractor.NumPoses);
+        }
+
+        [Test]
+        public void TestSingleBody()
+        {
+            var go = new GameObject();
+            var rootRb = go.AddComponent<Rigidbody>();
+            var poseExtractor = new RigidBodyPoseExtractor(rootRb);
+            Assert.AreEqual(1, poseExtractor.NumPoses);
+        }
+
+        [Test]
+        public void TestTwoBodies()
+        {
+            // * rootObj
+            //   - rb1
+            //   * go2
+            //     - rb2
+            //     - joint
+            var rootObj = new GameObject();
+            var rb1 = rootObj.AddComponent<Rigidbody>();
+
+            var go2 = new GameObject();
+            var rb2 = go2.AddComponent<Rigidbody>();
+            go2.transform.SetParent(rootObj.transform);
+
+            var joint = go2.AddComponent<ConfigurableJoint>();
+            joint.connectedBody = rb1;
+
+            var poseExtractor = new RigidBodyPoseExtractor(rb1);
+            Assert.AreEqual(2, poseExtractor.NumPoses);
+
+            rb1.position = new Vector3(1, 0, 0);
+            rb1.rotation = Quaternion.Euler(0, 13.37f, 0);
+            rb1.velocity = new Vector3(2, 0, 0);
+
+            Assert.AreEqual(rb1.position, poseExtractor.GetPoseAt(0).position);
+            Assert.IsTrue(rb1.rotation == poseExtractor.GetPoseAt(0).rotation);
+            Assert.AreEqual(rb1.velocity, poseExtractor.GetLinearVelocityAt(0));
+        }
+
+        [Test]
+        public void TestTwoBodiesVirtualRoot()
+        {
+            // * virtualRoot
+            // * rootObj
+            //   - rb1
+            //   * go2
+            //     - rb2
+            //     - joint
+            var virtualRoot = new GameObject("I am vroot");
+
+            var rootObj = new GameObject();
+            var rb1 = rootObj.AddComponent<Rigidbody>();
+
+            var go2 = new GameObject();
+            var rb2 = go2.AddComponent<Rigidbody>();
+            go2.transform.SetParent(rootObj.transform);
+
+            var joint = go2.AddComponent<ConfigurableJoint>();
+            joint.connectedBody = rb1;
+
+            var poseExtractor = new RigidBodyPoseExtractor(rb1, null, virtualRoot);
+            Assert.AreEqual(3, poseExtractor.NumPoses);
+
+            // "body" 0 has no parent
+            Assert.AreEqual(-1, poseExtractor.GetParentIndex(0));
+
+            // body 1 has parent 0
+            Assert.AreEqual(0, poseExtractor.GetParentIndex(1));
+
+            var virtualRootPos = new Vector3(0,2,0);
+            var virtualRootRot = Quaternion.Euler(0, 42, 0);
+            virtualRoot.transform.position = virtualRootPos;
+            virtualRoot.transform.rotation = virtualRootRot;
+
+            Assert.AreEqual(virtualRootPos, poseExtractor.GetPoseAt(0).position);
+            Assert.IsTrue(virtualRootRot == poseExtractor.GetPoseAt(0).rotation);
+            Assert.AreEqual(Vector3.zero, poseExtractor.GetLinearVelocityAt(0));
+
+            // Same as above test, but using index 1
+            rb1.position = new Vector3(1, 0, 0);
+            rb1.rotation = Quaternion.Euler(0, 13.37f, 0);
+            rb1.velocity = new Vector3(2, 0, 0);
+
+            Assert.AreEqual(rb1.position, poseExtractor.GetPoseAt(1).position);
+            Assert.IsTrue(rb1.rotation == poseExtractor.GetPoseAt(1).rotation);
+            Assert.AreEqual(rb1.velocity, poseExtractor.GetLinearVelocityAt(1));
+        }
+    }
+}

com.unity.ml-agents.extensions/Tests/Editor/Sensors/RigidBodySensorTests.cs

+using UnityEngine;
+using NUnit.Framework;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.Extensions.Sensors;
+
+
+namespace Unity.MLAgents.Extensions.Tests.Sensors
+{
+
+    public static class SensorTestHelper
+    {
+        public static void CompareObservation(ISensor sensor, float[] expected)
+        {
+            string errorMessage;
+            bool isOK = SensorHelper.CompareObservation(sensor, expected, out errorMessage);
+            Assert.IsTrue(isOK, errorMessage);
+        }
+    }
+
+    public class RigidBodySensorTests
+    {
+        [Test]
+        public void TestNullRootBody()
+        {
+            var gameObj = new GameObject();
+
+            var sensorComponent = gameObj.AddComponent<RigidBodySensorComponent>();
+            var sensor = sensorComponent.CreateSensor();
+            SensorTestHelper.CompareObservation(sensor, new float[0]);
+        }
+
+        [Test]
+        public void TestSingleRigidbody()
+        {
+            var gameObj = new GameObject();
+            var rootRb = gameObj.AddComponent<Rigidbody>();
+            var sensorComponent = gameObj.AddComponent<RigidBodySensorComponent>();
+            sensorComponent.RootBody = rootRb;
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseModelSpaceLinearVelocity = true,
+                UseLocalSpaceTranslations = true,
+                UseLocalSpaceRotations = true
+            };
+
+            var sensor = sensorComponent.CreateSensor();
+            sensor.Update();
+
+            // The root body is ignored since it always generates identity values
+            // and there are no other bodies to generate observations.
+            var expected = new float[0];
+            Assert.AreEqual(expected.Length, sensorComponent.GetObservationShape()[0]);
+            SensorTestHelper.CompareObservation(sensor, expected);
+        }
+
+        [Test]
+        public void TestBodiesWithJoint()
+        {
+            var rootObj = new GameObject();
+            var rootRb = rootObj.AddComponent<Rigidbody>();
+            rootRb.velocity = new Vector3(1f, 0f, 0f);
+
+            var middleGamObj = new GameObject();
+            var middleRb = middleGamObj.AddComponent<Rigidbody>();
+            middleRb.velocity = new Vector3(0f, 1f, 0f);
+            middleGamObj.transform.SetParent(rootObj.transform);
+            middleGamObj.transform.localPosition = new Vector3(13.37f, 0f, 0f);
+            var joint = middleGamObj.AddComponent<ConfigurableJoint>();
+            joint.connectedBody = rootRb;
+
+            var leafGameObj = new GameObject();
+            var leafRb = leafGameObj.AddComponent<Rigidbody>();
+            leafRb.velocity = new Vector3(0f, 0f, 1f);
+            leafGameObj.transform.SetParent(middleGamObj.transform);
+            leafGameObj.transform.localPosition = new Vector3(4.2f, 0f, 0f);
+            var joint2 = leafGameObj.AddComponent<ConfigurableJoint>();
+            joint2.connectedBody = middleRb;
+
+            var virtualRoot = new GameObject();
+
+            var sensorComponent = rootObj.AddComponent<RigidBodySensorComponent>();
+            sensorComponent.RootBody = rootRb;
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseModelSpaceTranslations = true,
+                UseLocalSpaceTranslations = true,
+                UseLocalSpaceLinearVelocity = true
+            };
+            sensorComponent.VirtualRoot = virtualRoot;
+
+            var sensor = sensorComponent.CreateSensor();
+            sensor.Update();
+
+            // Note that the VirtualRoot is ignored from the observations
+            var expected = new[]
+            {
+                // Model space
+                0f, 0f, 0f, // Root pos
+                13.37f, 0f, 0f, // Middle pos
+                leafGameObj.transform.position.x, 0f, 0f, // Leaf pos
+
+                // Local space
+                0f, 0f, 0f, // Root pos
+                13.37f, 0f, 0f, // Attached pos
+                4.2f, 0f, 0f, // Leaf pos
+
+                1f, 0f, 0f, // Root vel (relative to virtual root)
+                -1f, 1f, 0f, // Attached vel
+                0f, -1f, 1f // Leaf vel
+            };
+            Assert.AreEqual(expected.Length, sensorComponent.GetObservationShape()[0]);
+            SensorTestHelper.CompareObservation(sensor, expected);
+
+            // Update the settings to only process joint observations
+            sensorComponent.Settings = new PhysicsSensorSettings
+            {
+                UseJointPositionsAndAngles = true,
+                UseJointForces = true,
+            };
+
+            sensor = sensorComponent.CreateSensor();
+            sensor.Update();
+
+            expected = new[]
+            {
+                0f, 0f, 0f, // joint1.force
+                0f, 0f, 0f, // joint1.torque
+                0f, 0f, 0f, // joint2.force
+                0f, 0f, 0f, // joint2.torque
+            };
+            SensorTestHelper.CompareObservation(sensor, expected);
+            Assert.AreEqual(expected.Length, sensorComponent.GetObservationShape()[0]);
+
+        }
+    }
+}

ml-agents/mlagents/trainers/torch/decoders.py

+from typing import List, Dict
+
+import torch
+from torch import nn
+from mlagents.trainers.torch.layers import linear_layer
+
+
+class ValueHeads(nn.Module):
+    def __init__(self, stream_names: List[str], input_size: int, output_size: int = 1):
+        super().__init__()
+        self.stream_names = stream_names
+        _value_heads = {}
+
+        for name in stream_names:
+            value = linear_layer(input_size, output_size)
+            _value_heads[name] = value
+        self.value_heads = nn.ModuleDict(_value_heads)
+
+    def forward(self, hidden: torch.Tensor) -> Dict[str, torch.Tensor]:
+        value_outputs = {}
+        for stream_name, head in self.value_heads.items():
+            value_outputs[stream_name] = head(hidden).squeeze(-1)
+        return value_outputs

ml-agents/mlagents/trainers/torch/encoders.py

+from typing import Tuple, Optional, Union
+
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents.trainers.torch.layers import linear_layer, Initialization, Swish
+
+import torch
+from torch import nn
+
+
+class Normalizer(nn.Module):
+    def __init__(self, vec_obs_size: int):
+        super().__init__()
+        self.register_buffer("normalization_steps", torch.tensor(1))
+        self.register_buffer("running_mean", torch.zeros(vec_obs_size))
+        self.register_buffer("running_variance", torch.ones(vec_obs_size))
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        normalized_state = torch.clamp(
+            (inputs - self.running_mean)
+            / torch.sqrt(self.running_variance / self.normalization_steps),
+            -5,
+            5,
+        )
+        return normalized_state
+
+    def update(self, vector_input: torch.Tensor) -> None:
+        steps_increment = vector_input.size()[0]
+        total_new_steps = self.normalization_steps + steps_increment
+
+        input_to_old_mean = vector_input - self.running_mean
+        new_mean = self.running_mean + (input_to_old_mean / total_new_steps).sum(0)
+
+        input_to_new_mean = vector_input - new_mean
+        new_variance = self.running_variance + (
+            input_to_new_mean * input_to_old_mean
+        ).sum(0)
+        # Update in-place
+        self.running_mean.data.copy_(new_mean.data)
+        self.running_variance.data.copy_(new_variance.data)
+        self.normalization_steps.data.copy_(total_new_steps.data)
+
+    def copy_from(self, other_normalizer: "Normalizer") -> None:
+        self.normalization_steps.data.copy_(other_normalizer.normalization_steps.data)
+        self.running_mean.data.copy_(other_normalizer.running_mean.data)
+        self.running_variance.copy_(other_normalizer.running_variance.data)
+
+
+def conv_output_shape(
+    h_w: Tuple[int, int],
+    kernel_size: Union[int, Tuple[int, int]] = 1,
+    stride: int = 1,
+    padding: int = 0,
+    dilation: int = 1,
+) -> Tuple[int, int]:
+    """
+    Calculates the output shape (height and width) of the output of a convolution layer.
+    kernel_size, stride, padding and dilation correspond to the inputs of the
+    torch.nn.Conv2d layer (https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)
+    :param h_w: The height and width of the input.
+    :param kernel_size: The size of the kernel of the convolution (can be an int or a
+    tuple [width, height])
+    :param stride: The stride of the convolution
+    :param padding: The padding of the convolution
+    :param dilation: The dilation of the convolution
+    """
+    from math import floor
+
+    if not isinstance(kernel_size, tuple):
+        kernel_size = (int(kernel_size), int(kernel_size))
+    h = floor(
+        ((h_w[0] + (2 * padding) - (dilation * (kernel_size[0] - 1)) - 1) / stride) + 1
+    )
+    w = floor(
+        ((h_w[1] + (2 * padding) - (dilation * (kernel_size[1] - 1)) - 1) / stride) + 1
+    )
+    return h, w
+
+
+def pool_out_shape(h_w: Tuple[int, int], kernel_size: int) -> Tuple[int, int]:
+    """
+    Calculates the output shape (height and width) of the output of a max pooling layer.
+    kernel_size corresponds to the inputs of the
+    torch.nn.MaxPool2d layer (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html)
+    :param kernel_size: The size of the kernel of the convolution
+    """
+    height = (h_w[0] - kernel_size) // 2 + 1
+    width = (h_w[1] - kernel_size) // 2 + 1
+    return height, width
+
+
+class VectorEncoder(nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        num_layers: int,
+        normalize: bool = False,
+    ):
+        self.normalizer: Optional[Normalizer] = None
+        super().__init__()
+        self.layers = [
+            linear_layer(
+                input_size,
+                hidden_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=1.0,
+            )
+        ]
+        self.layers.append(Swish())
+        if normalize:
+            self.normalizer = Normalizer(input_size)
+
+        for _ in range(num_layers - 1):
+            self.layers.append(
+                linear_layer(
+                    hidden_size,
+                    hidden_size,
+                    kernel_init=Initialization.KaimingHeNormal,
+                    kernel_gain=1.0,
+                )
+            )
+            self.layers.append(Swish())
+        self.seq_layers = nn.Sequential(*self.layers)
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        if self.normalizer is not None:
+            inputs = self.normalizer(inputs)
+        return self.seq_layers(inputs)
+
+    def copy_normalization(self, other_encoder: "VectorEncoder") -> None:
+        if self.normalizer is not None and other_encoder.normalizer is not None:
+            self.normalizer.copy_from(other_encoder.normalizer)
+
+    def update_normalization(self, inputs: torch.Tensor) -> None:
+        if self.normalizer is not None:
+            self.normalizer.update(inputs)
+
+
+class VectorAndUnnormalizedInputEncoder(VectorEncoder):
+    """
+    Encoder for concatenated vector input (can be normalized) and unnormalized vector input.
+    This is used for passing inputs to the network that should not be normalized, such as
+    actions in the case of a Q function or task parameterizations. It will result in an encoder with
+    this structure:
+    ____________       ____________       ____________
+    | Vector     |     | Normalize  |     | Fully      |
+    |            | --> |            | --> | Connected  |      ___________
+    |____________|     |____________|     |            |     | Output    |
+    ____________                          |            | --> |           |
+    |Unnormalized|                        |            |     |___________|
+    |   Input    | ---------------------> |            |
+    |____________|                        |____________|
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        unnormalized_input_size: int,
+        num_layers: int,
+        normalize: bool = False,
+    ):
+        super().__init__(
+            input_size + unnormalized_input_size,
+            hidden_size,
+            num_layers,
+            normalize=False,
+        )
+        if normalize:
+            self.normalizer = Normalizer(input_size)
+        else:
+            self.normalizer = None
+
+    def forward(  # pylint: disable=W0221
+        self, inputs: torch.Tensor, unnormalized_inputs: Optional[torch.Tensor] = None
+    ) -> None:
+        if unnormalized_inputs is None:
+            raise UnityTrainerException(
+                "Attempted to call an VectorAndUnnormalizedInputEncoder without an unnormalized input."
+            )  # Fix mypy errors about method parameters.
+        if self.normalizer is not None:
+            inputs = self.normalizer(inputs)
+        return self.seq_layers(torch.cat([inputs, unnormalized_inputs], dim=-1))
+
+
+class SimpleVisualEncoder(nn.Module):
+    def __init__(
+        self, height: int, width: int, initial_channels: int, output_size: int
+    ):
+        super().__init__()
+        self.h_size = output_size
+        conv_1_hw = conv_output_shape((height, width), 8, 4)
+        conv_2_hw = conv_output_shape(conv_1_hw, 4, 2)
+        self.final_flat = conv_2_hw[0] * conv_2_hw[1] * 32
+
+        self.conv_layers = nn.Sequential(
+            nn.Conv2d(initial_channels, 16, [8, 8], [4, 4]),
+            nn.LeakyReLU(),
+            nn.Conv2d(16, 32, [4, 4], [2, 2]),
+            nn.LeakyReLU(),
+        )
+        self.dense = nn.Sequential(
+            linear_layer(
+                self.final_flat,
+                self.h_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=1.0,
+            ),
+            nn.LeakyReLU(),
+        )
+
+    def forward(self, visual_obs: torch.Tensor) -> None:
+        hidden = self.conv_layers(visual_obs)
+        hidden = torch.reshape(hidden, (-1, self.final_flat))
+        hidden = self.dense(hidden)
+        return hidden
+
+
+class NatureVisualEncoder(nn.Module):
+    def __init__(self, height, width, initial_channels, output_size):
+        super().__init__()
+        self.h_size = output_size
+        conv_1_hw = conv_output_shape((height, width), 8, 4)
+        conv_2_hw = conv_output_shape(conv_1_hw, 4, 2)
+        conv_3_hw = conv_output_shape(conv_2_hw, 3, 1)
+        self.final_flat = conv_3_hw[0] * conv_3_hw[1] * 64
+
+        self.conv_layers = nn.Sequential(
+            nn.Conv2d(initial_channels, 32, [8, 8], [4, 4]),
+            nn.LeakyReLU(),
+            nn.Conv2d(32, 64, [4, 4], [2, 2]),
+            nn.LeakyReLU(),
+            nn.Conv2d(64, 64, [3, 3], [1, 1]),
+            nn.LeakyReLU(),
+        )
+        self.dense = nn.Sequential(
+            linear_layer(
+                self.final_flat,
+                self.h_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=1.0,
+            ),
+            nn.LeakyReLU(),
+        )
+
+    def forward(self, visual_obs: torch.Tensor) -> None:
+        hidden = self.conv_layers(visual_obs)
+        hidden = hidden.view([-1, self.final_flat])
+        hidden = self.dense(hidden)
+        return hidden
+
+
+class ResNetBlock(nn.Module):
+    def __init__(self, channel: int):
+        """
+        Creates a ResNet Block.
+        :param channel: The number of channels in the input (and output) tensors of the
+        convolutions
+        """
+        super().__init__()
+        self.layers = nn.Sequential(
+            Swish(),
+            nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1),
+            Swish(),
+            nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1),
+        )
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        return input_tensor + self.layers(input_tensor)
+
+
+class ResNetVisualEncoder(nn.Module):
+    def __init__(self, height, width, initial_channels, final_hidden):
+        super().__init__()
+        n_channels = [16, 32, 32]  # channel for each stack
+        n_blocks = 2  # number of residual blocks
+        self.layers = []
+        last_channel = initial_channels
+        for _, channel in enumerate(n_channels):
+            self.layers.append(
+                nn.Conv2d(last_channel, channel, [3, 3], [1, 1], padding=1)
+            )
+            self.layers.append(nn.MaxPool2d([3, 3], [2, 2]))
+            height, width = pool_out_shape((height, width), 3)
+            for _ in range(n_blocks):
+                self.layers.append(ResNetBlock(channel))
+            last_channel = channel
+        self.layers.append(Swish())
+        self.dense = linear_layer(
+            n_channels[-1] * height * width,
+            final_hidden,
+            kernel_init=Initialization.KaimingHeNormal,
+            kernel_gain=1.0,
+        )
+
+    def forward(self, visual_obs):
+        batch_size = visual_obs.shape[0]
+        hidden = visual_obs
+        for layer in self.layers:
+            hidden = layer(hidden)
+        before_out = hidden.view(batch_size, -1)
+        return torch.relu(self.dense(before_out))

ml-agents/mlagents/trainers/torch/networks.py

+from typing import Callable, List, Dict, Tuple, Optional
+import attr
+import abc
+
+import torch
+from torch import nn
+
+from mlagents_envs.base_env import ActionType
+from mlagents.trainers.torch.distributions import (
+    GaussianDistribution,
+    MultiCategoricalDistribution,
+    DistInstance,
+)
+from mlagents.trainers.settings import NetworkSettings
+from mlagents.trainers.torch.utils import ModelUtils
+from mlagents.trainers.torch.decoders import ValueHeads
+
+ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
+EncoderFunction = Callable[
+    [torch.Tensor, int, ActivationFunction, int, str, bool], torch.Tensor
+]
+
+EPSILON = 1e-7
+
+
+class NetworkBody(nn.Module):
+    def __init__(
+        self,
+        observation_shapes: List[Tuple[int, ...]],
+        network_settings: NetworkSettings,
+        encoded_act_size: int = 0,
+    ):
+        super().__init__()
+        self.normalize = network_settings.normalize
+        self.use_lstm = network_settings.memory is not None
+        self.h_size = network_settings.hidden_units
+        self.m_size = (
+            network_settings.memory.memory_size
+            if network_settings.memory is not None
+            else 0
+        )
+
+        self.visual_encoders, self.vector_encoders = ModelUtils.create_encoders(
+            observation_shapes,
+            self.h_size,
+            network_settings.num_layers,
+            network_settings.vis_encode_type,
+            unnormalized_inputs=encoded_act_size,
+            normalize=self.normalize,
+        )
+
+        if self.use_lstm:
+            self.lstm = nn.LSTM(self.h_size, self.m_size // 2, 1)
+        else:
+            self.lstm = None
+
+    def update_normalization(self, vec_inputs: List[torch.Tensor]) -> None:
+        for vec_input, vec_enc in zip(vec_inputs, self.vector_encoders):
+            vec_enc.update_normalization(vec_input)
+
+    def copy_normalization(self, other_network: "NetworkBody") -> None:
+        if self.normalize:
+            for n1, n2 in zip(self.vector_encoders, other_network.vector_encoders):
+                n1.copy_normalization(n2)
+
+    def forward(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        actions: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        vec_encodes = []
+        for idx, encoder in enumerate(self.vector_encoders):
+            vec_input = vec_inputs[idx]
+            if actions is not None:
+                hidden = encoder(vec_input, actions)
+            else:
+                hidden = encoder(vec_input)
+            vec_encodes.append(hidden)
+
+        vis_encodes = []
+        for idx, encoder in enumerate(self.visual_encoders):
+            vis_input = vis_inputs[idx]
+            vis_input = vis_input.permute([0, 3, 1, 2])
+            hidden = encoder(vis_input)
+            vis_encodes.append(hidden)
+
+        if len(vec_encodes) > 0 and len(vis_encodes) > 0:
+            vec_encodes_tensor = torch.stack(vec_encodes, dim=-1).sum(dim=-1)
+            vis_encodes_tensor = torch.stack(vis_encodes, dim=-1).sum(dim=-1)
+            encoding = torch.stack(
+                [vec_encodes_tensor, vis_encodes_tensor], dim=-1
+            ).sum(dim=-1)
+        elif len(vec_encodes) > 0:
+            encoding = torch.stack(vec_encodes, dim=-1).sum(dim=-1)
+        elif len(vis_encodes) > 0:
+            encoding = torch.stack(vis_encodes, dim=-1).sum(dim=-1)
+        else:
+            raise Exception("No valid inputs to network.")
+
+        if self.use_lstm:
+            encoding = encoding.view([sequence_length, -1, self.h_size])
+            memories = torch.split(memories, self.m_size // 2, dim=-1)
+            encoding, memories = self.lstm(
+                encoding.contiguous(),
+                (memories[0].contiguous(), memories[1].contiguous()),
+            )
+            encoding = encoding.view([-1, self.m_size // 2])
+            memories = torch.cat(memories, dim=-1)
+        return encoding, memories
+
+
+class ValueNetwork(nn.Module):
+    def __init__(
+        self,
+        stream_names: List[str],
+        observation_shapes: List[Tuple[int, ...]],
+        network_settings: NetworkSettings,
+        encoded_act_size: int = 0,
+        outputs_per_stream: int = 1,
+    ):
+
+        # This is not a typo, we want to call __init__ of nn.Module
+        nn.Module.__init__(self)
+        self.network_body = NetworkBody(
+            observation_shapes, network_settings, encoded_act_size=encoded_act_size
+        )
+        if network_settings.memory is not None:
+            encoding_size = network_settings.memory.memory_size // 2
+        else:
+            encoding_size = network_settings.hidden_units
+        self.value_heads = ValueHeads(stream_names, encoding_size, outputs_per_stream)
+
+    def forward(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        actions: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+        encoding, memories = self.network_body(
+            vec_inputs, vis_inputs, actions, memories, sequence_length
+        )
+        output = self.value_heads(encoding)
+        return output, memories
+
+
+class Actor(abc.ABC):
+    @abc.abstractmethod
+    def update_normalization(self, vector_obs: List[torch.Tensor]) -> None:
+        """
+        Updates normalization of Actor based on the provided List of vector obs.
+        :param vector_obs: A List of vector obs as tensors.
+        """
+        pass
+
+    @abc.abstractmethod
+    def sample_action(self, dists: List[DistInstance]) -> List[torch.Tensor]:
+        """
+        Takes a List of Distribution iinstances and samples an action from each.
+        """
+        pass
+
+    @abc.abstractmethod
+    def get_dists(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[List[DistInstance], Optional[torch.Tensor]]:
+        """
+        Returns distributions from this Actor, from which actions can be sampled.
+        If memory is enabled, return the memories as well.
+        :param vec_inputs: A List of vector inputs as tensors.
+        :param vis_inputs: A List of visual inputs as tensors.
+        :param masks: If using discrete actions, a Tensor of action masks.
+        :param memories: If using memory, a Tensor of initial memories.
+        :param sequence_length: If using memory, the sequence length.
+        :return: A Tuple of a List of action distribution instances, and memories.
+            Memories will be None if not using memory.
+        """
+        pass
+
+    @abc.abstractmethod
+    def forward(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[torch.Tensor, torch.Tensor, int, int, int, int]:
+        """
+        Forward pass of the Actor for inference. This is required for export to ONNX, and
+        the inputs and outputs of this method should not be changed without a respective change
+        in the ONNX export code.
+        """
+        pass
+
+
+class ActorCritic(Actor):
+    @abc.abstractmethod
+    def critic_pass(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        memories: Optional[torch.Tensor] = None,
+    ) -> Dict[str, torch.Tensor]:
+        """
+        Get value outputs for the given obs.
+        :param vec_inputs: List of vector inputs as tensors.
+        :param vis_inputs: List of visual inputs as tensors.
+        :param memories: Tensor of memories, if using memory. Otherwise, None.
+        :returns: Dict of reward stream to output tensor for values.
+        """
+        pass
+
+    @abc.abstractmethod
+    def get_dist_and_value(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[List[DistInstance], Dict[str, torch.Tensor], torch.Tensor]:
+        """
+        Returns distributions, from which actions can be sampled, and value estimates.
+        If memory is enabled, return the memories as well.
+        :param vec_inputs: A List of vector inputs as tensors.
+        :param vis_inputs: A List of visual inputs as tensors.
+        :param masks: If using discrete actions, a Tensor of action masks.
+        :param memories: If using memory, a Tensor of initial memories.
+        :param sequence_length: If using memory, the sequence length.
+        :return: A Tuple of a List of action distribution instances, a Dict of reward signal
+            name to value estimate, and memories. Memories will be None if not using memory.
+        """
+        pass
+
+
+class SimpleActor(nn.Module, Actor):
+    def __init__(
+        self,
+        observation_shapes: List[Tuple[int, ...]],
+        network_settings: NetworkSettings,
+        act_type: ActionType,
+        act_size: List[int],
+        conditional_sigma: bool = False,
+        tanh_squash: bool = False,
+    ):
+        super().__init__()
+        self.act_type = act_type
+        self.act_size = act_size
+        self.version_number = torch.nn.Parameter(torch.Tensor([2.0]))
+        self.memory_size = torch.nn.Parameter(torch.Tensor([0]))
+        self.is_continuous_int = torch.nn.Parameter(
+            torch.Tensor([int(act_type == ActionType.CONTINUOUS)])
+        )
+        self.act_size_vector = torch.nn.Parameter(torch.Tensor(act_size))
+        self.network_body = NetworkBody(observation_shapes, network_settings)
+        if network_settings.memory is not None:
+            self.encoding_size = network_settings.memory.memory_size // 2
+        else:
+            self.encoding_size = network_settings.hidden_units
+        if self.act_type == ActionType.CONTINUOUS:
+            self.distribution = GaussianDistribution(
+                self.encoding_size,
+                act_size[0],
+                conditional_sigma=conditional_sigma,
+                tanh_squash=tanh_squash,
+            )
+        else:
+            self.distribution = MultiCategoricalDistribution(
+                self.encoding_size, act_size
+            )
+
+    def update_normalization(self, vector_obs: List[torch.Tensor]) -> None:
+        self.network_body.update_normalization(vector_obs)
+
+    def sample_action(self, dists: List[DistInstance]) -> List[torch.Tensor]:
+        actions = []
+        for action_dist in dists:
+            action = action_dist.sample()
+            actions.append(action)
+        return actions
+
+    def get_dists(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[List[DistInstance], Optional[torch.Tensor]]:
+        encoding, memories = self.network_body(
+            vec_inputs, vis_inputs, memories=memories, sequence_length=sequence_length
+        )
+        if self.act_type == ActionType.CONTINUOUS:
+            dists = self.distribution(encoding)
+        else:
+            dists = self.distribution(encoding, masks)
+
+        return dists, memories
+
+    def forward(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[torch.Tensor, torch.Tensor, int, int, int, int]:
+        """
+        Note: This forward() method is required for exporting to ONNX. Don't modify the inputs and outputs.
+        """
+        dists, _ = self.get_dists(
+            vec_inputs, vis_inputs, masks, memories, sequence_length
+        )
+        action_list = self.sample_action(dists)
+        sampled_actions = torch.stack(action_list, dim=-1)
+        return (
+            sampled_actions,
+            dists[0].pdf(sampled_actions),
+            self.version_number,
+            self.memory_size,
+            self.is_continuous_int,
+            self.act_size_vector,
+        )
+
+
+class SharedActorCritic(SimpleActor, ActorCritic):
+    def __init__(
+        self,
+        observation_shapes: List[Tuple[int, ...]],
+        network_settings: NetworkSettings,
+        act_type: ActionType,
+        act_size: List[int],
+        stream_names: List[str],
+        conditional_sigma: bool = False,
+        tanh_squash: bool = False,
+    ):
+        super().__init__(
+            observation_shapes,
+            network_settings,
+            act_type,
+            act_size,
+            conditional_sigma,
+            tanh_squash,
+        )
+        self.stream_names = stream_names
+        self.value_heads = ValueHeads(stream_names, self.encoding_size)
+
+    def critic_pass(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        memories: Optional[torch.Tensor] = None,
+    ) -> Dict[str, torch.Tensor]:
+        encoding, _ = self.network_body(vec_inputs, vis_inputs, memories=memories)
+        return self.value_heads(encoding)
+
+    def get_dist_and_value(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[List[DistInstance], Dict[str, torch.Tensor], torch.Tensor]:
+        encoding, memories = self.network_body(
+            vec_inputs, vis_inputs, memories=memories, sequence_length=sequence_length
+        )
+        if self.act_type == ActionType.CONTINUOUS:
+            dists = self.distribution(encoding)
+        else:
+            dists = self.distribution(encoding, masks=masks)
+
+        value_outputs = self.value_heads(encoding)
+        return dists, value_outputs, memories
+
+
+class SeparateActorCritic(SimpleActor, ActorCritic):
+    def __init__(
+        self,
+        observation_shapes: List[Tuple[int, ...]],
+        network_settings: NetworkSettings,
+        act_type: ActionType,
+        act_size: List[int],
+        stream_names: List[str],
+        conditional_sigma: bool = False,
+        tanh_squash: bool = False,
+    ):
+        # Give the Actor only half the memories. Note we previously validate
+        # that memory_size must be a multiple of 4.
+        self.use_lstm = network_settings.memory is not None
+        if network_settings.memory is not None:
+            self.half_mem_size = network_settings.memory.memory_size // 2
+            new_memory_settings = attr.evolve(
+                network_settings.memory, memory_size=self.half_mem_size
+            )
+            use_network_settings = attr.evolve(
+                network_settings, memory=new_memory_settings
+            )
+        else:
+            use_network_settings = network_settings
+            self.half_mem_size = 0
+        super().__init__(
+            observation_shapes,
+            use_network_settings,
+            act_type,
+            act_size,
+            conditional_sigma,
+            tanh_squash,
+        )
+        self.stream_names = stream_names
+        self.critic = ValueNetwork(
+            stream_names, observation_shapes, use_network_settings
+        )
+
+    def critic_pass(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        memories: Optional[torch.Tensor] = None,
+    ) -> Dict[str, torch.Tensor]:
+        if self.use_lstm:
+            # Use only the back half of memories for critic
+            _, critic_mem = torch.split(memories, self.half_mem_size, -1)
+        else:
+            critic_mem = None
+        value_outputs, _memories = self.critic(
+            vec_inputs, vis_inputs, memories=critic_mem
+        )
+        return value_outputs
+
+    def get_dist_and_value(
+        self,
+        vec_inputs: List[torch.Tensor],
+        vis_inputs: List[torch.Tensor],
+        masks: Optional[torch.Tensor] = None,
+        memories: Optional[torch.Tensor] = None,
+        sequence_length: int = 1,
+    ) -> Tuple[List[DistInstance], Dict[str, torch.Tensor], torch.Tensor]:
+        if self.use_lstm:
+            # Use only the back half of memories for critic and actor
+            actor_mem, critic_mem = torch.split(memories, self.half_mem_size, dim=-1)
+        else:
+            critic_mem = None
+            actor_mem = None
+        dists, actor_mem_outs = self.get_dists(
+            vec_inputs,
+            vis_inputs,
+            memories=actor_mem,
+            sequence_length=sequence_length,
+            masks=masks,
+        )
+        value_outputs, critic_mem_outs = self.critic(
+            vec_inputs, vis_inputs, memories=critic_mem, sequence_length=sequence_length
+        )
+        if self.use_lstm:
+            mem_out = torch.cat([actor_mem_outs, critic_mem_outs], dim=1)
+        else:
+            mem_out = None
+        return dists, value_outputs, mem_out
+
+
+class GlobalSteps(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.global_step = torch.Tensor([0])
+
+    def increment(self, value):
+        self.global_step += value
+
+
+class LearningRate(nn.Module):
+    def __init__(self, lr):
+        # Todo: add learning rate decay
+        super().__init__()
+        self.learning_rate = torch.Tensor([lr])

ml-agents/mlagents/trainers/torch/distributions.py

+import abc
+from typing import List
+import torch
+from torch import nn
+import numpy as np
+import math
+from mlagents.trainers.torch.layers import linear_layer, Initialization
+
+EPSILON = 1e-7  # Small value to avoid divide by zero
+
+
+class DistInstance(nn.Module, abc.ABC):
+    @abc.abstractmethod
+    def sample(self) -> torch.Tensor:
+        """
+        Return a sample from this distribution.
+        """
+        pass
+
+    @abc.abstractmethod
+    def log_prob(self, value: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the log probabilities of a particular value.
+        :param value: A value sampled from the distribution.
+        :returns: Log probabilities of the given value.
+        """
+        pass
+
+    @abc.abstractmethod
+    def entropy(self) -> torch.Tensor:
+        """
+        Returns the entropy of this distribution.
+        """
+        pass
+
+
+class DiscreteDistInstance(DistInstance):
+    @abc.abstractmethod
+    def all_log_prob(self) -> torch.Tensor:
+        """
+        Returns the log probabilities of all actions represented by this distribution.
+        """
+        pass
+
+
+class GaussianDistInstance(DistInstance):
+    def __init__(self, mean, std):
+        super().__init__()
+        self.mean = mean
+        self.std = std
+
+    def sample(self):
+        sample = self.mean + torch.randn_like(self.mean) * self.std
+        return sample
+
+    def log_prob(self, value):
+        var = self.std ** 2
+        log_scale = torch.log(self.std + EPSILON)
+        return (
+            -((value - self.mean) ** 2) / (2 * var + EPSILON)
+            - log_scale
+            - math.log(math.sqrt(2 * math.pi))
+        )
+
+    def pdf(self, value):
+        log_prob = self.log_prob(value)
+        return torch.exp(log_prob)
+
+    def entropy(self):
+        return 0.5 * torch.log(2 * math.pi * math.e * self.std + EPSILON)
+
+
+class TanhGaussianDistInstance(GaussianDistInstance):
+    def __init__(self, mean, std):
+        super().__init__(mean, std)
+        self.transform = torch.distributions.transforms.TanhTransform(cache_size=1)
+
+    def sample(self):
+        unsquashed_sample = super().sample()
+        squashed = self.transform(unsquashed_sample)
+        return squashed
+
+    def _inverse_tanh(self, value):
+        capped_value = torch.clamp(value, -1 + EPSILON, 1 - EPSILON)
+        return 0.5 * torch.log((1 + capped_value) / (1 - capped_value) + EPSILON)
+
+    def log_prob(self, value):
+        unsquashed = self.transform.inv(value)
+        return super().log_prob(unsquashed) - self.transform.log_abs_det_jacobian(
+            unsquashed, value
+        )
+
+
+class CategoricalDistInstance(DiscreteDistInstance):
+    def __init__(self, logits):
+        super().__init__()
+        self.logits = logits
+        self.probs = torch.softmax(self.logits, dim=-1)
+
+    def sample(self):
+        return torch.multinomial(self.probs, 1)
+
+    def pdf(self, value):
+        # This function is equivalent to torch.diag(self.probs.T[value.flatten().long()]),
+        # but torch.diag is not supported by ONNX export.
+        idx = torch.arange(start=0, end=len(value)).unsqueeze(-1)
+        return torch.gather(
+            self.probs.permute(1, 0)[value.flatten().long()], -1, idx
+        ).squeeze(-1)
+
+    def log_prob(self, value):
+        return torch.log(self.pdf(value))
+
+    def all_log_prob(self):
+        return torch.log(self.probs)
+
+    def entropy(self):
+        return -torch.sum(self.probs * torch.log(self.probs), dim=-1)
+
+
+class GaussianDistribution(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_outputs: int,
+        conditional_sigma: bool = False,
+        tanh_squash: bool = False,
+    ):
+        super().__init__()
+        self.conditional_sigma = conditional_sigma
+        self.mu = linear_layer(
+            hidden_size,
+            num_outputs,
+            kernel_init=Initialization.KaimingHeNormal,
+            kernel_gain=0.1,
+            bias_init=Initialization.Zero,
+        )
+        self.tanh_squash = tanh_squash
+        if conditional_sigma:
+            self.log_sigma = linear_layer(
+                hidden_size,
+                num_outputs,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=0.1,
+                bias_init=Initialization.Zero,
+            )
+        else:
+            self.log_sigma = nn.Parameter(
+                torch.zeros(1, num_outputs, requires_grad=True)
+            )
+
+    def forward(self, inputs: torch.Tensor) -> List[DistInstance]:
+        mu = self.mu(inputs)
+        if self.conditional_sigma:
+            log_sigma = torch.clamp(self.log_sigma(inputs), min=-20, max=2)
+        else:
+            log_sigma = self.log_sigma
+        if self.tanh_squash:
+            return [TanhGaussianDistInstance(mu, torch.exp(log_sigma))]
+        else:
+            return [GaussianDistInstance(mu, torch.exp(log_sigma))]
+
+
+class MultiCategoricalDistribution(nn.Module):
+    def __init__(self, hidden_size: int, act_sizes: List[int]):
+        super().__init__()
+        self.act_sizes = act_sizes
+        self.branches = self._create_policy_branches(hidden_size)
+
+    def _create_policy_branches(self, hidden_size: int) -> nn.ModuleList:
+        branches = []
+        for size in self.act_sizes:
+            branch_output_layer = linear_layer(
+                hidden_size,
+                size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=0.1,
+                bias_init=Initialization.Zero,
+            )
+            branches.append(branch_output_layer)
+        return nn.ModuleList(branches)
+
+    def _mask_branch(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+        raw_probs = torch.nn.functional.softmax(logits, dim=-1) * mask
+        normalized_probs = raw_probs / torch.sum(raw_probs, dim=-1).unsqueeze(-1)
+        normalized_logits = torch.log(normalized_probs + EPSILON)
+        return normalized_logits
+
+    def _split_masks(self, masks: torch.Tensor) -> List[torch.Tensor]:
+        split_masks = []
+        for idx, _ in enumerate(self.act_sizes):
+            start = int(np.sum(self.act_sizes[:idx]))
+            end = int(np.sum(self.act_sizes[: idx + 1]))
+            split_masks.append(masks[:, start:end])
+        return split_masks
+
+    def forward(self, inputs: torch.Tensor, masks: torch.Tensor) -> List[DistInstance]:
+        # Todo - Support multiple branches in mask code
+        branch_distributions = []
+        masks = self._split_masks(masks)
+        for idx, branch in enumerate(self.branches):
+            logits = branch(inputs)
+            norm_logits = self._mask_branch(logits, masks[idx])
+            distribution = CategoricalDistInstance(norm_logits)
+            branch_distributions.append(distribution)
+        return branch_distributions

ml-agents/mlagents/trainers/torch/utils.py

+from typing import List, Optional, Tuple
+import torch
+import numpy as np
+from torch import nn
+
+from mlagents.trainers.torch.encoders import (
+    SimpleVisualEncoder,
+    ResNetVisualEncoder,
+    NatureVisualEncoder,
+    VectorEncoder,
+    VectorAndUnnormalizedInputEncoder,
+)
+from mlagents.trainers.settings import EncoderType, ScheduleType
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.torch.distributions import DistInstance, DiscreteDistInstance
+
+
+class ModelUtils:
+    # Minimum supported side for each encoder type. If refactoring an encoder, please
+    # adjust these also.
+    MIN_RESOLUTION_FOR_ENCODER = {
+        EncoderType.SIMPLE: 20,
+        EncoderType.NATURE_CNN: 36,
+        EncoderType.RESNET: 15,
+    }
+
+    class ActionFlattener:
+        def __init__(self, behavior_spec: BehaviorSpec):
+            self._specs = behavior_spec
+
+        @property
+        def flattened_size(self) -> int:
+            if self._specs.is_action_continuous():
+                return self._specs.action_size
+            else:
+                return sum(self._specs.discrete_action_branches)
+
+        def forward(self, action: torch.Tensor) -> torch.Tensor:
+            if self._specs.is_action_continuous():
+                return action
+            else:
+                return torch.cat(
+                    ModelUtils.actions_to_onehot(
+                        torch.as_tensor(action, dtype=torch.long),
+                        self._specs.discrete_action_branches,
+                    ),
+                    dim=1,
+                )
+
+    @staticmethod
+    def update_learning_rate(optim: torch.optim.Optimizer, lr: float) -> None:
+        """
+        Apply a learning rate to a torch optimizer.
+        :param optim: Optimizer
+        :param lr: Learning rate
+        """
+        for param_group in optim.param_groups:
+            param_group["lr"] = lr
+
+    class DecayedValue:
+        def __init__(
+            self,
+            schedule: ScheduleType,
+            initial_value: float,
+            min_value: float,
+            max_step: int,
+        ):
+            """
+            Object that represnets value of a parameter that should be decayed, assuming it is a function of
+            global_step.
+            :param schedule: Type of learning rate schedule.
+            :param initial_value: Initial value before decay.
+            :param min_value: Decay value to this value by max_step.
+            :param max_step: The final step count where the return value should equal min_value.
+            :param global_step: The current step count.
+            :return: The value.
+            """
+            self.schedule = schedule
+            self.initial_value = initial_value
+            self.min_value = min_value
+            self.max_step = max_step
+
+        def get_value(self, global_step: int) -> float:
+            """
+            Get the value at a given global step.
+            :param global_step: Step count.
+            :returns: Decayed value at this global step.
+            """
+            if self.schedule == ScheduleType.CONSTANT:
+                return self.initial_value
+            elif self.schedule == ScheduleType.LINEAR:
+                return ModelUtils.polynomial_decay(
+                    self.initial_value, self.min_value, self.max_step, global_step
+                )
+            else:
+                raise UnityTrainerException(f"The schedule {self.schedule} is invalid.")
+
+    @staticmethod
+    def polynomial_decay(
+        initial_value: float,
+        min_value: float,
+        max_step: int,
+        global_step: int,
+        power: float = 1.0,
+    ) -> float:
+        """
+        Get a decayed value based on a polynomial schedule, with respect to the current global step.
+        :param initial_value: Initial value before decay.
+        :param min_value: Decay value to this value by max_step.
+        :param max_step: The final step count where the return value should equal min_value.
+        :param global_step: The current step count.
+        :param power: Power of polynomial decay. 1.0 (default) is a linear decay.
+        :return: The current decayed value.
+        """
+        global_step = min(global_step, max_step)
+        decayed_value = (initial_value - min_value) * (
+            1 - float(global_step) / max_step
+        ) ** (power) + min_value
+        return decayed_value
+
+    @staticmethod
+    def get_encoder_for_type(encoder_type: EncoderType) -> nn.Module:
+        ENCODER_FUNCTION_BY_TYPE = {
+            EncoderType.SIMPLE: SimpleVisualEncoder,
+            EncoderType.NATURE_CNN: NatureVisualEncoder,
+            EncoderType.RESNET: ResNetVisualEncoder,
+        }
+        return ENCODER_FUNCTION_BY_TYPE.get(encoder_type)
+
+    @staticmethod
+    def _check_resolution_for_encoder(
+        height: int, width: int, vis_encoder_type: EncoderType
+    ) -> None:
+        min_res = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[vis_encoder_type]
+        if height < min_res or width < min_res:
+            raise UnityTrainerException(
+                f"Visual observation resolution ({width}x{height}) is too small for"
+                f"the provided EncoderType ({vis_encoder_type.value}). The min dimension is {min_res}"
+            )
+
+    @staticmethod
+    def create_encoders(
+        observation_shapes: List[Tuple[int, ...]],
+        h_size: int,
+        num_layers: int,
+        vis_encode_type: EncoderType,
+        unnormalized_inputs: int = 0,
+        normalize: bool = False,
+    ) -> Tuple[nn.ModuleList, nn.ModuleList]:
+        """
+        Creates visual and vector encoders, along with their normalizers.
+        :param observation_shapes: List of Tuples that represent the action dimensions.
+        :param action_size: Number of additional un-normalized inputs to each vector encoder. Used for
+            conditioining network on other values (e.g. actions for a Q function)
+        :param h_size: Number of hidden units per layer.
+        :param num_layers: Depth of MLP per encoder.
+        :param vis_encode_type: Type of visual encoder to use.
+        :param unnormalized_inputs: Vector inputs that should not be normalized, and added to the vector
+            obs.
+        :param normalize: Normalize all vector inputs.
+        :return: Tuple of visual encoders and vector encoders each as a list.
+        """
+        visual_encoders: List[nn.Module] = []
+        vector_encoders: List[nn.Module] = []
+
+        visual_encoder_class = ModelUtils.get_encoder_for_type(vis_encode_type)
+        vector_size = 0
+        for i, dimension in enumerate(observation_shapes):
+            if len(dimension) == 3:
+                ModelUtils._check_resolution_for_encoder(
+                    dimension[0], dimension[1], vis_encode_type
+                )
+                visual_encoders.append(
+                    visual_encoder_class(
+                        dimension[0], dimension[1], dimension[2], h_size
+                    )
+                )
+            elif len(dimension) == 1:
+                vector_size += dimension[0]
+            else:
+                raise UnityTrainerException(
+                    f"Unsupported shape of {dimension} for observation {i}"
+                )
+        if vector_size + unnormalized_inputs > 0:
+            if unnormalized_inputs > 0:
+                vector_encoders.append(
+                    VectorAndUnnormalizedInputEncoder(
+                        vector_size, h_size, unnormalized_inputs, num_layers, normalize
+                    )
+                )
+            else:
+                vector_encoders.append(
+                    VectorEncoder(vector_size, h_size, num_layers, normalize)
+                )
+        return nn.ModuleList(visual_encoders), nn.ModuleList(vector_encoders)
+
+    @staticmethod
+    def list_to_tensor(
+        ndarray_list: List[np.ndarray], dtype: Optional[torch.dtype] = None
+    ) -> torch.Tensor:
+        """
+        Converts a list of numpy arrays into a tensor. MUCH faster than
+        calling as_tensor on the list directly.
+        """
+        return torch.as_tensor(np.asanyarray(ndarray_list), dtype=dtype)
+
+    @staticmethod
+    def break_into_branches(
+        concatenated_logits: torch.Tensor, action_size: List[int]
+    ) -> List[torch.Tensor]:
+        """
+        Takes a concatenated set of logits that represent multiple discrete action branches
+        and breaks it up into one Tensor per branch.
+        :param concatenated_logits: Tensor that represents the concatenated action branches
+        :param action_size: List of ints containing the number of possible actions for each branch.
+        :return: A List of Tensors containing one tensor per branch.
+        """
+        action_idx = [0] + list(np.cumsum(action_size))
+        branched_logits = [
+            concatenated_logits[:, action_idx[i] : action_idx[i + 1]]
+            for i in range(len(action_size))
+        ]
+        return branched_logits
+
+    @staticmethod
+    def actions_to_onehot(
+        discrete_actions: torch.Tensor, action_size: List[int]
+    ) -> List[torch.Tensor]:
+        """
+        Takes a tensor of discrete actions and turns it into a List of onehot encoding for each
+        action.
+        :param discrete_actions: Actions in integer form.
+        :param action_size: List of branch sizes. Should be of same size as discrete_actions'
+        last dimension.
+        :return: List of one-hot tensors, one representing each branch.
+        """
+        onehot_branches = [
+            torch.nn.functional.one_hot(_act.T, action_size[i]).float()
+            for i, _act in enumerate(discrete_actions.long().T)
+        ]
+        return onehot_branches
+
+    @staticmethod
+    def dynamic_partition(
+        data: torch.Tensor, partitions: torch.Tensor, num_partitions: int
+    ) -> List[torch.Tensor]:
+        """
+        Torch implementation of dynamic_partition :
+        https://www.tensorflow.org/api_docs/python/tf/dynamic_partition
+        Splits the data Tensor input into num_partitions Tensors according to the indices in
+        partitions.
+        :param data: The Tensor data that will be split into partitions.
+        :param partitions: An indices tensor that determines in which partition each element
+        of data will be in.
+        :param num_partitions: The number of partitions to output. Corresponds to the
+        maximum possible index in the partitions argument.
+        :return: A list of Tensor partitions (Their indices correspond to their partition index).
+        """
+        res: List[torch.Tensor] = []
+        for i in range(num_partitions):
+            res += [data[(partitions == i).nonzero().squeeze(1)]]
+        return res
+
+    @staticmethod
+    def get_probs_and_entropy(
+        action_list: List[torch.Tensor], dists: List[DistInstance]
+    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+        log_probs_list = []
+        all_probs_list = []
+        entropies_list = []
+        for action, action_dist in zip(action_list, dists):
+            log_prob = action_dist.log_prob(action)
+            log_probs_list.append(log_prob)
+            entropies_list.append(action_dist.entropy())
+            if isinstance(action_dist, DiscreteDistInstance):
+                all_probs_list.append(action_dist.all_log_prob())
+        log_probs = torch.stack(log_probs_list, dim=-1)
+        entropies = torch.stack(entropies_list, dim=-1)
+        if not all_probs_list:
+            log_probs = log_probs.squeeze(-1)
+            entropies = entropies.squeeze(-1)
+            all_probs = None
+        else:
+            all_probs = torch.cat(all_probs_list, dim=-1)
+        return log_probs, entropies, all_probs

ml-agents/mlagents/trainers/torch/layers.py

+import torch
+from enum import Enum
+
+
+class Swish(torch.nn.Module):
+    def forward(self, data: torch.Tensor) -> torch.Tensor:
+        return torch.mul(data, torch.sigmoid(data))
+
+
+class Initialization(Enum):
+    Zero = 0
+    XavierGlorotNormal = 1
+    XavierGlorotUniform = 2
+    KaimingHeNormal = 3  # also known as Variance scaling
+    KaimingHeUniform = 4
+
+
+_init_methods = {
+    Initialization.Zero: torch.zero_,
+    Initialization.XavierGlorotNormal: torch.nn.init.xavier_normal_,
+    Initialization.XavierGlorotUniform: torch.nn.init.xavier_uniform_,
+    Initialization.KaimingHeNormal: torch.nn.init.kaiming_normal_,
+    Initialization.KaimingHeUniform: torch.nn.init.kaiming_uniform_,
+}
+
+
+def linear_layer(
+    input_size: int,
+    output_size: int,
+    kernel_init: Initialization = Initialization.XavierGlorotUniform,
+    kernel_gain: float = 1.0,
+    bias_init: Initialization = Initialization.Zero,
+) -> torch.nn.Module:
+    """
+    Creates a torch.nn.Linear module and initializes its weights.
+    :param input_size: The size of the input tensor
+    :param output_size: The size of the output tensor
+    :param kernel_init: The Initialization to use for the weights of the layer
+    :param kernel_gain: The multiplier for the weights of the kernel. Note that in
+    TensorFlow, calling variance_scaling with scale 0.01 is equivalent to calling
+    KaimingHeNormal with kernel_gain of 0.1
+    :param bias_init: The Initialization to use for the weights of the bias layer
+    """
+    layer = torch.nn.Linear(input_size, output_size)
+    _init_methods[kernel_init](layer.weight.data)
+    layer.weight.data *= kernel_gain
+    _init_methods[bias_init](layer.bias.data)
+    return layer

ml-agents/mlagents/trainers/torch/components/reward_providers/__init__.py

+from mlagents.trainers.torch.components.reward_providers.base_reward_provider import (  # noqa F401
+    BaseRewardProvider,
+)
+from mlagents.trainers.torch.components.reward_providers.extrinsic_reward_provider import (  # noqa F401
+    ExtrinsicRewardProvider,
+)
+from mlagents.trainers.torch.components.reward_providers.curiosity_reward_provider import (  # noqa F401
+    CuriosityRewardProvider,
+)
+from mlagents.trainers.torch.components.reward_providers.gail_reward_provider import (  # noqa F401
+    GAILRewardProvider,
+)
+from mlagents.trainers.torch.components.reward_providers.reward_provider_factory import (  # noqa F401
+    create_reward_provider,
+)

ml-agents/mlagents/trainers/torch/components/reward_providers/base_reward_provider.py

+import numpy as np
+from abc import ABC, abstractmethod
+from typing import Dict
+
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.settings import RewardSignalSettings
+from mlagents_envs.base_env import BehaviorSpec
+
+
+class BaseRewardProvider(ABC):
+    def __init__(self, specs: BehaviorSpec, settings: RewardSignalSettings) -> None:
+        self._policy_specs = specs
+        self._gamma = settings.gamma
+        self._strength = settings.strength
+        self._ignore_done = False
+
+    @property
+    def gamma(self) -> float:
+        """
+        The discount factor for the reward signal
+        """
+        return self._gamma
+
+    @property
+    def strength(self) -> float:
+        """
+        The strength multiplier of the reward provider
+        """
+        return self._strength
+
+    @property
+    def name(self) -> str:
+        """
+        The name of the reward provider. Is used for reporting and identification
+        """
+        class_name = self.__class__.__name__
+        return class_name.replace("RewardProvider", "")
+
+    @property
+    def ignore_done(self) -> bool:
+        """
+        If true, when the agent is done, the rewards of the next episode must be
+        used to calculate the return of the current episode.
+        Is used to mitigate the positive bias in rewards with no natural end.
+        """
+        return self._ignore_done
+
+    @abstractmethod
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        """
+        Evaluates the reward for the data present in the Dict mini_batch. Use this when evaluating a reward
+        function drawn straight from a Buffer.
+        :param mini_batch: A Dict of numpy arrays (the format used by our Buffer)
+            when drawing from the update buffer.
+        :return: a np.ndarray of rewards generated by the reward provider
+        """
+        raise NotImplementedError(
+            "The reward provider's evaluate method has not been implemented "
+        )
+
+    @abstractmethod
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        """
+        Update the reward for the data present in the Dict mini_batch. Use this when updating a reward
+        function drawn straight from a Buffer.
+        :param mini_batch: A Dict of numpy arrays (the format used by our Buffer)
+            when drawing from the update buffer.
+        :return: A dictionary from string to stats values
+        """
+        raise NotImplementedError(
+            "The reward provider's update method has not been implemented "
+        )

ml-agents/mlagents/trainers/torch/components/reward_providers/extrinsic_reward_provider.py

+import numpy as np
+from typing import Dict
+
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.torch.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+
+
+class ExtrinsicRewardProvider(BaseRewardProvider):
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        return np.array(mini_batch["environment_rewards"], dtype=np.float32)
+
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        return {}

ml-agents/mlagents/trainers/torch/components/reward_providers/reward_provider_factory.py

+from typing import Dict, Type
+from mlagents.trainers.exception import UnityTrainerException
+
+from mlagents.trainers.settings import RewardSignalSettings, RewardSignalType
+
+from mlagents.trainers.torch.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents.trainers.torch.components.reward_providers.extrinsic_reward_provider import (
+    ExtrinsicRewardProvider,
+)
+from mlagents.trainers.torch.components.reward_providers.curiosity_reward_provider import (
+    CuriosityRewardProvider,
+)
+from mlagents.trainers.torch.components.reward_providers.gail_reward_provider import (
+    GAILRewardProvider,
+)
+
+from mlagents_envs.base_env import BehaviorSpec
+
+NAME_TO_CLASS: Dict[RewardSignalType, Type[BaseRewardProvider]] = {
+    RewardSignalType.EXTRINSIC: ExtrinsicRewardProvider,
+    RewardSignalType.CURIOSITY: CuriosityRewardProvider,
+    RewardSignalType.GAIL: GAILRewardProvider,
+}
+
+
+def create_reward_provider(
+    name: RewardSignalType, specs: BehaviorSpec, settings: RewardSignalSettings
+) -> BaseRewardProvider:
+    """
+    Creates a reward provider class based on the name and config entry provided as a dict.
+    :param name: The name of the reward signal
+    :param specs: The BehaviorSpecs of the policy
+    :param settings: The RewardSignalSettings for that reward signal
+    :return: The reward signal class instantiated
+    """
+    rcls = NAME_TO_CLASS.get(name)
+    if not rcls:
+        raise UnityTrainerException(f"Unknown reward signal type {name}")
+
+    class_inst = rcls(specs, settings)
+    return class_inst

ml-agents/mlagents/trainers/torch/components/reward_providers/curiosity_reward_provider.py

+import numpy as np
+from typing import Dict
+import torch
+
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.torch.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents.trainers.settings import CuriositySettings
+
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.torch.utils import ModelUtils
+from mlagents.trainers.torch.networks import NetworkBody
+from mlagents.trainers.torch.layers import linear_layer, Swish
+from mlagents.trainers.settings import NetworkSettings, EncoderType
+
+
+class CuriosityRewardProvider(BaseRewardProvider):
+    beta = 0.2  # Forward vs Inverse loss weight
+    loss_multiplier = 10.0  # Loss multiplier
+
+    def __init__(self, specs: BehaviorSpec, settings: CuriositySettings) -> None:
+        super().__init__(specs, settings)
+        self._ignore_done = True
+        self._network = CuriosityNetwork(specs, settings)
+        self.optimizer = torch.optim.Adam(
+            self._network.parameters(), lr=settings.learning_rate
+        )
+        self._has_updated_once = False
+
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        with torch.no_grad():
+            rewards = self._network.compute_reward(mini_batch).detach().cpu().numpy()
+        rewards = np.minimum(rewards, 1.0 / self.strength)
+        return rewards * self._has_updated_once
+
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        self._has_updated_once = True
+        forward_loss = self._network.compute_forward_loss(mini_batch)
+        inverse_loss = self._network.compute_inverse_loss(mini_batch)
+
+        loss = self.loss_multiplier * (
+            self.beta * forward_loss + (1.0 - self.beta) * inverse_loss
+        )
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        return {
+            "Losses/Curiosity Forward Loss": forward_loss.detach().cpu().numpy(),
+            "Losses/Curiosity Inverse Loss": inverse_loss.detach().cpu().numpy(),
+        }
+
+
+class CuriosityNetwork(torch.nn.Module):
+    EPSILON = 1e-10
+
+    def __init__(self, specs: BehaviorSpec, settings: CuriositySettings) -> None:
+        super().__init__()
+        self._policy_specs = specs
+        state_encoder_settings = NetworkSettings(
+            normalize=False,
+            hidden_units=settings.encoding_size,
+            num_layers=2,
+            vis_encode_type=EncoderType.SIMPLE,
+            memory=None,
+        )
+        self._state_encoder = NetworkBody(
+            specs.observation_shapes, state_encoder_settings
+        )
+
+        self._action_flattener = ModelUtils.ActionFlattener(specs)
+
+        self.inverse_model_action_predition = torch.nn.Sequential(
+            linear_layer(2 * settings.encoding_size, 256),
+            Swish(),
+            linear_layer(256, self._action_flattener.flattened_size),
+        )
+
+        self.forward_model_next_state_prediction = torch.nn.Sequential(
+            linear_layer(
+                settings.encoding_size + self._action_flattener.flattened_size, 256
+            ),
+            Swish(),
+            linear_layer(256, settings.encoding_size),
+        )
+
+    def get_current_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Extracts the current state embedding from a mini_batch.
+        """
+        n_vis = len(self._state_encoder.visual_encoders)
+        hidden, _ = self._state_encoder.forward(
+            vec_inputs=[
+                ModelUtils.list_to_tensor(mini_batch["vector_obs"], dtype=torch.float)
+            ],
+            vis_inputs=[
+                ModelUtils.list_to_tensor(
+                    mini_batch["visual_obs%d" % i], dtype=torch.float
+                )
+                for i in range(n_vis)
+            ],
+        )
+        return hidden
+
+    def get_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Extracts the next state embedding from a mini_batch.
+        """
+        n_vis = len(self._state_encoder.visual_encoders)
+        hidden, _ = self._state_encoder.forward(
+            vec_inputs=[
+                ModelUtils.list_to_tensor(
+                    mini_batch["next_vector_in"], dtype=torch.float
+                )
+            ],
+            vis_inputs=[
+                ModelUtils.list_to_tensor(
+                    mini_batch["next_visual_obs%d" % i], dtype=torch.float
+                )
+                for i in range(n_vis)
+            ],
+        )
+        return hidden
+
+    def predict_action(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        In the continuous case, returns the predicted action.
+        In the discrete case, returns the logits.
+        """
+        inverse_model_input = torch.cat(
+            (self.get_current_state(mini_batch), self.get_next_state(mini_batch)), dim=1
+        )
+        hidden = self.inverse_model_action_predition(inverse_model_input)
+        if self._policy_specs.is_action_continuous():
+            return hidden
+        else:
+            branches = ModelUtils.break_into_branches(
+                hidden, self._policy_specs.discrete_action_branches
+            )
+            branches = [torch.softmax(b, dim=1) for b in branches]
+            return torch.cat(branches, dim=1)
+
+    def predict_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Uses the current state embedding and the action of the mini_batch to predict
+        the next state embedding.
+        """
+        if self._policy_specs.is_action_continuous():
+            action = ModelUtils.list_to_tensor(mini_batch["actions"], dtype=torch.float)
+        else:
+            action = torch.cat(
+                ModelUtils.actions_to_onehot(
+                    ModelUtils.list_to_tensor(mini_batch["actions"], dtype=torch.long),
+                    self._policy_specs.discrete_action_branches,
+                ),
+                dim=1,
+            )
+        forward_model_input = torch.cat(
+            (self.get_current_state(mini_batch), action), dim=1
+        )
+
+        return self.forward_model_next_state_prediction(forward_model_input)
+
+    def compute_inverse_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Computes the inverse loss for a mini_batch. Corresponds to the error on the
+        action prediction (given the current and next state).
+        """
+        predicted_action = self.predict_action(mini_batch)
+        if self._policy_specs.is_action_continuous():
+            sq_difference = (
+                ModelUtils.list_to_tensor(mini_batch["actions"], dtype=torch.float)
+                - predicted_action
+            ) ** 2
+            sq_difference = torch.sum(sq_difference, dim=1)
+            return torch.mean(
+                ModelUtils.dynamic_partition(
+                    sq_difference,
+                    ModelUtils.list_to_tensor(mini_batch["masks"], dtype=torch.float),
+                    2,
+                )[1]
+            )
+        else:
+            true_action = torch.cat(
+                ModelUtils.actions_to_onehot(
+                    ModelUtils.list_to_tensor(mini_batch["actions"], dtype=torch.long),
+                    self._policy_specs.discrete_action_branches,
+                ),
+                dim=1,
+            )
+            cross_entropy = torch.sum(
+                -torch.log(predicted_action + self.EPSILON) * true_action, dim=1
+            )
+            return torch.mean(
+                ModelUtils.dynamic_partition(
+                    cross_entropy,
+                    ModelUtils.list_to_tensor(
+                        mini_batch["masks"], dtype=torch.float
+                    ),  # use masks not action_masks
+                    2,
+                )[1]
+            )
+
+    def compute_reward(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Calculates the curiosity reward for the mini_batch. Corresponds to the error
+        between the predicted and actual next state.
+        """
+        predicted_next_state = self.predict_next_state(mini_batch)
+        target = self.get_next_state(mini_batch)
+        sq_difference = 0.5 * (target - predicted_next_state) ** 2
+        sq_difference = torch.sum(sq_difference, dim=1)
+        return sq_difference
+
+    def compute_forward_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Computes the loss for the next state prediction
+        """
+        return torch.mean(
+            ModelUtils.dynamic_partition(
+                self.compute_reward(mini_batch),
+                ModelUtils.list_to_tensor(mini_batch["masks"], dtype=torch.float),
+                2,
+            )[1]
+        )

ml-agents/mlagents/trainers/torch/components/reward_providers/gail_reward_provider.py

+from typing import Optional, Dict
+import numpy as np
+import torch
+
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.torch.components.reward_providers.base_reward_provider import (
+    BaseRewardProvider,
+)
+from mlagents.trainers.settings import GAILSettings
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.torch.utils import ModelUtils
+from mlagents.trainers.torch.networks import NetworkBody
+from mlagents.trainers.torch.layers import linear_layer, Swish, Initialization
+from mlagents.trainers.settings import NetworkSettings, EncoderType
+from mlagents.trainers.demo_loader import demo_to_buffer
+
+
+class GAILRewardProvider(BaseRewardProvider):
+    def __init__(self, specs: BehaviorSpec, settings: GAILSettings) -> None:
+        super().__init__(specs, settings)
+        self._ignore_done = True
+        self._discriminator_network = DiscriminatorNetwork(specs, settings)
+        _, self._demo_buffer = demo_to_buffer(
+            settings.demo_path, 1, specs
+        )  # This is supposed to be the sequence length but we do not have access here
+        params = list(self._discriminator_network.parameters())
+        self.optimizer = torch.optim.Adam(params, lr=settings.learning_rate)
+
+    def evaluate(self, mini_batch: AgentBuffer) -> np.ndarray:
+        with torch.no_grad():
+            estimates, _ = self._discriminator_network.compute_estimate(
+                mini_batch, use_vail_noise=False
+            )
+            return (
+                -torch.log(
+                    1.0
+                    - estimates.squeeze(dim=1)
+                    * (1.0 - self._discriminator_network.EPSILON)
+                )
+                .detach()
+                .cpu()
+                .numpy()
+            )
+
+    def update(self, mini_batch: AgentBuffer) -> Dict[str, np.ndarray]:
+        expert_batch = self._demo_buffer.sample_mini_batch(
+            mini_batch.num_experiences, 1
+        )
+        loss, policy_mean_estimate, expert_mean_estimate, kl_loss = self._discriminator_network.compute_loss(
+            mini_batch, expert_batch
+        )
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        stats_dict = {
+            "Losses/GAIL Discriminator Loss": loss.detach().cpu().numpy(),
+            "Policy/GAIL Policy Estimate": policy_mean_estimate.detach().cpu().numpy(),
+            "Policy/GAIL Expert Estimate": expert_mean_estimate.detach().cpu().numpy(),
+        }
+        if self._discriminator_network.use_vail:
+            stats_dict["Policy/GAIL Beta"] = (
+                self._discriminator_network.beta.detach().cpu().numpy()
+            )
+            stats_dict["Losses/GAIL KL Loss"] = kl_loss.detach().cpu().numpy()
+        return stats_dict
+
+
+class DiscriminatorNetwork(torch.nn.Module):
+    gradient_penalty_weight = 10.0
+    z_size = 128
+    alpha = 0.0005
+    mutual_information = 0.5
+    EPSILON = 1e-7
+    initial_beta = 0.0
+
+    def __init__(self, specs: BehaviorSpec, settings: GAILSettings) -> None:
+        super().__init__()
+        self._policy_specs = specs
+        self.use_vail = settings.use_vail
+        self._settings = settings
+
+        state_encoder_settings = NetworkSettings(
+            normalize=False,
+            hidden_units=settings.encoding_size,
+            num_layers=2,
+            vis_encode_type=EncoderType.SIMPLE,
+            memory=None,
+        )
+        self._state_encoder = NetworkBody(
+            specs.observation_shapes, state_encoder_settings
+        )
+
+        self._action_flattener = ModelUtils.ActionFlattener(specs)
+
+        encoder_input_size = settings.encoding_size
+        if settings.use_actions:
+            encoder_input_size += (
+                self._action_flattener.flattened_size + 1
+            )  # + 1 is for done
+
+        self.encoder = torch.nn.Sequential(
+            linear_layer(encoder_input_size, settings.encoding_size),
+            Swish(),
+            linear_layer(settings.encoding_size, settings.encoding_size),
+            Swish(),
+        )
+
+        estimator_input_size = settings.encoding_size
+        if settings.use_vail:
+            estimator_input_size = self.z_size
+            self.z_sigma = torch.nn.Parameter(
+                torch.ones((self.z_size), dtype=torch.float), requires_grad=True
+            )
+            self.z_mu_layer = linear_layer(
+                settings.encoding_size,
+                self.z_size,
+                kernel_init=Initialization.KaimingHeNormal,
+                kernel_gain=0.1,
+            )
+            self.beta = torch.nn.Parameter(
+                torch.tensor(self.initial_beta, dtype=torch.float), requires_grad=False
+            )
+
+        self.estimator = torch.nn.Sequential(
+            linear_layer(estimator_input_size, 1), torch.nn.Sigmoid()
+        )
+
+    def get_action_input(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Creates the action Tensor. In continuous case, corresponds to the action. In
+        the discrete case, corresponds to the concatenation of one hot action Tensors.
+        """
+        return self._action_flattener.forward(
+            torch.as_tensor(mini_batch["actions"], dtype=torch.float)
+        )
+
+    def get_state_encoding(self, mini_batch: AgentBuffer) -> torch.Tensor:
+        """
+        Creates the observation input.
+        """
+        n_vis = len(self._state_encoder.visual_encoders)
+        hidden, _ = self._state_encoder.forward(
+            vec_inputs=[torch.as_tensor(mini_batch["vector_obs"], dtype=torch.float)],
+            vis_inputs=[
+                torch.as_tensor(mini_batch["visual_obs%d" % i], dtype=torch.float)
+                for i in range(n_vis)
+            ],
+        )
+        return hidden
+
+    def compute_estimate(
+        self, mini_batch: AgentBuffer, use_vail_noise: bool = False
+    ) -> torch.Tensor:
+        """
+        Given a mini_batch, computes the estimate (How much the discriminator believes
+        the data was sampled from the demonstration data).
+        :param mini_batch: The AgentBuffer of data
+        :param use_vail_noise: Only when using VAIL : If true, will sample the code, if
+        false, will return the mean of the code.
+        """
+        encoder_input = self.get_state_encoding(mini_batch)
+        if self._settings.use_actions:
+            actions = self.get_action_input(mini_batch)
+            dones = torch.as_tensor(mini_batch["done"], dtype=torch.float)
+            encoder_input = torch.cat([encoder_input, actions, dones], dim=1)
+        hidden = self.encoder(encoder_input)
+        z_mu: Optional[torch.Tensor] = None
+        if self._settings.use_vail:
+            z_mu = self.z_mu_layer(hidden)
+            hidden = torch.normal(z_mu, self.z_sigma * use_vail_noise)
+        estimate = self.estimator(hidden)
+        return estimate, z_mu
+
+    def compute_loss(
+        self, policy_batch: AgentBuffer, expert_batch: AgentBuffer
+    ) -> torch.Tensor:
+        """
+        Given a policy mini_batch and an expert mini_batch, computes the loss of the discriminator.
+        """
+        policy_estimate, policy_mu = self.compute_estimate(
+            policy_batch, use_vail_noise=True
+        )
+        expert_estimate, expert_mu = self.compute_estimate(
+            expert_batch, use_vail_noise=True
+        )
+        loss = -(
+            torch.log(expert_estimate * (1 - self.EPSILON))
+            + torch.log(1.0 - policy_estimate * (1 - self.EPSILON))
+        ).mean()
+        kl_loss: Optional[torch.Tensor] = None
+        if self._settings.use_vail:
+            # KL divergence loss (encourage latent representation to be normal)
+            kl_loss = torch.mean(
+                -torch.sum(
+                    1
+                    + (self.z_sigma ** 2).log()
+                    - 0.5 * expert_mu ** 2
+                    - 0.5 * policy_mu ** 2
+                    - (self.z_sigma ** 2),
+                    dim=1,
+                )
+            )
+            vail_loss = self.beta * (kl_loss - self.mutual_information)
+            with torch.no_grad():
+                self.beta.data = torch.max(
+                    self.beta + self.alpha * (kl_loss - self.mutual_information),
+                    torch.tensor(0.0),
+                )
+            loss += vail_loss
+        if self.gradient_penalty_weight > 0.0:
+            loss += self.gradient_penalty_weight * self.compute_gradient_magnitude(
+                policy_batch, expert_batch
+            )
+        return loss, torch.mean(policy_estimate), torch.mean(expert_estimate), kl_loss
+
+    def compute_gradient_magnitude(
+        self, policy_batch: AgentBuffer, expert_batch: AgentBuffer
+    ) -> torch.Tensor:
+        """
+        Gradient penalty from https://arxiv.org/pdf/1704.00028. Adds stability esp.
+        for off-policy. Compute gradients w.r.t randomly interpolated input.
+        """
+        policy_obs = self.get_state_encoding(policy_batch)
+        expert_obs = self.get_state_encoding(expert_batch)
+        obs_epsilon = torch.rand(policy_obs.shape)
+        encoder_input = obs_epsilon * policy_obs + (1 - obs_epsilon) * expert_obs
+        if self._settings.use_actions:
+            policy_action = self.get_action_input(policy_batch)
+            expert_action = self.get_action_input(policy_batch)
+            action_epsilon = torch.rand(policy_action.shape)
+            policy_dones = torch.as_tensor(policy_batch["done"], dtype=torch.float)
+            expert_dones = torch.as_tensor(expert_batch["done"], dtype=torch.float)
+            dones_epsilon = torch.rand(policy_dones.shape)
+            encoder_input = torch.cat(
+                [
+                    encoder_input,
+                    action_epsilon * policy_action
+                    + (1 - action_epsilon) * expert_action,
+                    dones_epsilon * policy_dones + (1 - dones_epsilon) * expert_dones,
+                ],
+                dim=1,
+            )
+        hidden = self.encoder(encoder_input)
+        if self._settings.use_vail:
+            use_vail_noise = True
+            z_mu = self.z_mu_layer(hidden)
+            hidden = torch.normal(z_mu, self.z_sigma * use_vail_noise)
+        hidden = self.estimator(hidden)
+        estimate = torch.mean(torch.sum(hidden, dim=1))
+        gradient = torch.autograd.grad(estimate, encoder_input)[0]
+        # Norm's gradient could be NaN at 0. Use our own safe_norm
+        safe_norm = (torch.sum(gradient ** 2, dim=1) + self.EPSILON).sqrt()
+        gradient_mag = torch.mean((safe_norm - 1) ** 2)
+        return gradient_mag

ml-agents/mlagents/trainers/torch/components/bc/module.py

+from typing import Dict
+import numpy as np
+import torch
+
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.demo_loader import demo_to_buffer
+from mlagents.trainers.settings import BehavioralCloningSettings, ScheduleType
+from mlagents.trainers.torch.utils import ModelUtils
+
+
+class BCModule:
+    def __init__(
+        self,
+        policy: TorchPolicy,
+        settings: BehavioralCloningSettings,
+        policy_learning_rate: float,
+        default_batch_size: int,
+        default_num_epoch: int,
+    ):
+        """
+        A BC trainer that can be used inline with RL.
+        :param policy: The policy of the learning model
+        :param settings: The settings for BehavioralCloning including LR strength, batch_size,
+        num_epochs, samples_per_update and LR annealing steps.
+        :param policy_learning_rate: The initial Learning Rate of the policy. Used to set an appropriate learning rate
+            for the pretrainer.
+        """
+        self.policy = policy
+        self._anneal_steps = settings.steps
+        self.current_lr = policy_learning_rate * settings.strength
+
+        learning_rate_schedule: ScheduleType = ScheduleType.LINEAR if self._anneal_steps > 0 else ScheduleType.CONSTANT
+        self.decay_learning_rate = ModelUtils.DecayedValue(
+            learning_rate_schedule, self.current_lr, 1e-10, self._anneal_steps
+        )
+        params = self.policy.actor_critic.parameters()
+        self.optimizer = torch.optim.Adam(params, lr=self.current_lr)
+        _, self.demonstration_buffer = demo_to_buffer(
+            settings.demo_path, policy.sequence_length, policy.behavior_spec
+        )
+
+        self.batch_size = (
+            settings.batch_size if settings.batch_size else default_batch_size
+        )
+        self.num_epoch = settings.num_epoch if settings.num_epoch else default_num_epoch
+        self.n_sequences = max(
+            min(self.batch_size, self.demonstration_buffer.num_experiences)
+            // policy.sequence_length,
+            1,
+        )
+
+        self.has_updated = False
+        self.use_recurrent = self.policy.use_recurrent
+        self.samples_per_update = settings.samples_per_update
+
+    def update(self) -> Dict[str, np.ndarray]:
+        """
+        Updates model using buffer.
+        :param max_batches: The maximum number of batches to use per update.
+        :return: The loss of the update.
+        """
+        # Don't continue training if the learning rate has reached 0, to reduce training time.
+
+        decay_lr = self.decay_learning_rate.get_value(self.policy.get_current_step())
+        if self.current_lr <= 0:
+            return {"Losses/Pretraining Loss": 0}
+
+        batch_losses = []
+        possible_demo_batches = (
+            self.demonstration_buffer.num_experiences // self.n_sequences
+        )
+        possible_batches = possible_demo_batches
+
+        max_batches = self.samples_per_update // self.n_sequences
+
+        n_epoch = self.num_epoch
+        for _ in range(n_epoch):
+            self.demonstration_buffer.shuffle(
+                sequence_length=self.policy.sequence_length
+            )
+            if max_batches == 0:
+                num_batches = possible_batches
+            else:
+                num_batches = min(possible_batches, max_batches)
+            for i in range(num_batches // self.policy.sequence_length):
+                demo_update_buffer = self.demonstration_buffer
+                start = i * self.n_sequences * self.policy.sequence_length
+                end = (i + 1) * self.n_sequences * self.policy.sequence_length
+                mini_batch_demo = demo_update_buffer.make_mini_batch(start, end)
+                run_out = self._update_batch(mini_batch_demo, self.n_sequences)
+                loss = run_out["loss"]
+                batch_losses.append(loss)
+
+        ModelUtils.update_learning_rate(self.optimizer, decay_lr)
+        self.current_lr = decay_lr
+
+        self.has_updated = True
+        update_stats = {"Losses/Pretraining Loss": np.mean(batch_losses)}
+        return update_stats
+
+    def _behavioral_cloning_loss(self, selected_actions, log_probs, expert_actions):
+        if self.policy.use_continuous_act:
+            bc_loss = torch.nn.functional.mse_loss(selected_actions, expert_actions)
+        else:
+            log_prob_branches = ModelUtils.break_into_branches(
+                log_probs, self.policy.act_size
+            )
+            bc_loss = torch.mean(
+                torch.stack(
+                    [
+                        torch.sum(
+                            -torch.nn.functional.log_softmax(log_prob_branch, dim=1)
+                            * expert_actions_branch,
+                            dim=1,
+                        )
+                        for log_prob_branch, expert_actions_branch in zip(
+                            log_prob_branches, expert_actions
+                        )
+                    ]
+                )
+            )
+        return bc_loss
+
+    def _update_batch(
+        self, mini_batch_demo: Dict[str, np.ndarray], n_sequences: int
+    ) -> Dict[str, float]:
+        """
+        Helper function for update_batch.
+        """
+        vec_obs = [ModelUtils.list_to_tensor(mini_batch_demo["vector_obs"])]
+        act_masks = None
+        if self.policy.use_continuous_act:
+            expert_actions = ModelUtils.list_to_tensor(mini_batch_demo["actions"])
+        else:
+            raw_expert_actions = ModelUtils.list_to_tensor(
+                mini_batch_demo["actions"], dtype=torch.long
+            )
+            expert_actions = ModelUtils.actions_to_onehot(
+                raw_expert_actions, self.policy.act_size
+            )
+            act_masks = ModelUtils.list_to_tensor(
+                np.ones(
+                    (
+                        self.n_sequences * self.policy.sequence_length,
+                        sum(self.policy.behavior_spec.discrete_action_branches),
+                    ),
+                    dtype=np.float32,
+                )
+            )
+
+        memories = []
+        if self.policy.use_recurrent:
+            memories = torch.zeros(
+                1, self.n_sequences, self.policy.actor_critic.half_mem_size * 2
+            )
+
+        if self.policy.use_vis_obs:
+            vis_obs = []
+            for idx, _ in enumerate(
+                self.policy.actor_critic.network_body.visual_encoders
+            ):
+                vis_ob = ModelUtils.list_to_tensor(
+                    mini_batch_demo["visual_obs%d" % idx]
+                )
+                vis_obs.append(vis_ob)
+        else:
+            vis_obs = []
+
+        selected_actions, all_log_probs, _, _, _ = self.policy.sample_actions(
+            vec_obs,
+            vis_obs,
+            masks=act_masks,
+            memories=memories,
+            seq_len=self.policy.sequence_length,
+            all_log_probs=True,
+        )
+        bc_loss = self._behavioral_cloning_loss(
+            selected_actions, all_log_probs, expert_actions
+        )
+        self.optimizer.zero_grad()
+        bc_loss.backward()
+
+        self.optimizer.step()
+        run_out = {"loss": bc_loss.detach().cpu().numpy()}
+        return run_out

ml-agents/mlagents/trainers/tests/torch/test_networks.py

+import pytest
+
+import torch
+from mlagents.trainers.torch.networks import (
+    NetworkBody,
+    ValueNetwork,
+    SimpleActor,
+    SharedActorCritic,
+    SeparateActorCritic,
+)
+from mlagents.trainers.settings import NetworkSettings
+from mlagents_envs.base_env import ActionType
+from mlagents.trainers.torch.distributions import (
+    GaussianDistInstance,
+    CategoricalDistInstance,
+)
+
+
+def test_networkbody_vector():
+    torch.manual_seed(0)
+    obs_size = 4
+    network_settings = NetworkSettings()
+    obs_shapes = [(obs_size,)]
+
+    networkbody = NetworkBody(obs_shapes, network_settings, encoded_act_size=2)
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
+    sample_obs = 0.1 * torch.ones((1, obs_size))
+    sample_act = 0.1 * torch.ones((1, 2))
+
+    for _ in range(300):
+        encoded, _ = networkbody([sample_obs], [], sample_act)
+        assert encoded.shape == (1, network_settings.hidden_units)
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+def test_networkbody_lstm():
+    torch.manual_seed(0)
+    obs_size = 4
+    seq_len = 16
+    network_settings = NetworkSettings(
+        memory=NetworkSettings.MemorySettings(sequence_length=seq_len, memory_size=4)
+    )
+    obs_shapes = [(obs_size,)]
+
+    networkbody = NetworkBody(obs_shapes, network_settings)
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
+    sample_obs = torch.ones((1, seq_len, obs_size))
+
+    for _ in range(100):
+        encoded, _ = networkbody([sample_obs], [], memories=torch.ones(1, seq_len, 4))
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+def test_networkbody_visual():
+    torch.manual_seed(0)
+    vec_obs_size = 4
+    obs_size = (84, 84, 3)
+    network_settings = NetworkSettings()
+    obs_shapes = [(vec_obs_size,), obs_size]
+    torch.random.manual_seed(0)
+
+    networkbody = NetworkBody(obs_shapes, network_settings)
+    optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
+    sample_obs = torch.ones((1, 84, 84, 3))
+    sample_vec_obs = torch.ones((1, vec_obs_size))
+
+    for _ in range(150):
+        encoded, _ = networkbody([sample_vec_obs], [sample_obs])
+        assert encoded.shape == (1, network_settings.hidden_units)
+        # Try to force output to 1
+        loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for _enc in encoded.flatten():
+        assert _enc == pytest.approx(1.0, abs=0.1)
+
+
+def test_valuenetwork():
+    torch.manual_seed(0)
+    obs_size = 4
+    num_outputs = 2
+    network_settings = NetworkSettings()
+    obs_shapes = [(obs_size,)]
+
+    stream_names = [f"stream_name{n}" for n in range(4)]
+    value_net = ValueNetwork(
+        stream_names, obs_shapes, network_settings, outputs_per_stream=num_outputs
+    )
+    optimizer = torch.optim.Adam(value_net.parameters(), lr=3e-3)
+
+    for _ in range(50):
+        sample_obs = torch.ones((1, obs_size))
+        values, _ = value_net([sample_obs], [])
+        loss = 0
+        for s_name in stream_names:
+            assert values[s_name].shape == (1, num_outputs)
+            # Try to force output to 1
+            loss += torch.nn.functional.mse_loss(
+                values[s_name], torch.ones((1, num_outputs))
+            )
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    # In the last step, values should be close to 1
+    for value in values.values():
+        for _out in value:
+            assert _out[0] == pytest.approx(1.0, abs=0.1)
+
+
+@pytest.mark.parametrize("action_type", [ActionType.DISCRETE, ActionType.CONTINUOUS])
+def test_simple_actor(action_type):
+    obs_size = 4
+    network_settings = NetworkSettings()
+    obs_shapes = [(obs_size,)]
+    act_size = [2]
+    masks = None if action_type == ActionType.CONTINUOUS else torch.ones((1, 1))
+    actor = SimpleActor(obs_shapes, network_settings, action_type, act_size)
+    # Test get_dist
+    sample_obs = torch.ones((1, obs_size))
+    dists, _ = actor.get_dists([sample_obs], [], masks=masks)
+    for dist in dists:
+        if action_type == ActionType.CONTINUOUS:
+            assert isinstance(dist, GaussianDistInstance)
+        else:
+            assert isinstance(dist, CategoricalDistInstance)
+
+    # Test sample_actions
+    actions = actor.sample_action(dists)
+    for act in actions:
+        if action_type == ActionType.CONTINUOUS:
+            assert act.shape == (1, act_size[0])
+        else:
+            assert act.shape == (1, 1)
+
+    # Test forward
+    actions, probs, ver_num, mem_size, is_cont, act_size_vec = actor.forward(
+        [sample_obs], [], masks=masks
+    )
+    for act in actions:
+        if action_type == ActionType.CONTINUOUS:
+            assert act.shape == (
+                act_size[0],
+                1,
+            )  # This is different from above for ONNX export
+        else:
+            assert act.shape == (1, 1)
+
+    # TODO: Once export works properly. fix the shapes here.
+    assert mem_size == 0
+    assert is_cont == int(action_type == ActionType.CONTINUOUS)
+    assert act_size_vec == torch.tensor(act_size)
+
+
+@pytest.mark.parametrize("ac_type", [SharedActorCritic, SeparateActorCritic])
+@pytest.mark.parametrize("lstm", [True, False])
+def test_actor_critic(ac_type, lstm):
+    obs_size = 4
+    network_settings = NetworkSettings(
+        memory=NetworkSettings.MemorySettings() if lstm else None
+    )
+    obs_shapes = [(obs_size,)]
+    act_size = [2]
+    stream_names = [f"stream_name{n}" for n in range(4)]
+    actor = ac_type(
+        obs_shapes, network_settings, ActionType.CONTINUOUS, act_size, stream_names
+    )
+    if lstm:
+        sample_obs = torch.ones((1, network_settings.memory.sequence_length, obs_size))
+        memories = torch.ones(
+            (
+                1,
+                network_settings.memory.sequence_length,
+                network_settings.memory.memory_size,
+            )
+        )
+    else:
+        sample_obs = torch.ones((1, obs_size))
+        memories = torch.tensor([])
+        # memories isn't always set to None, the network should be able to
+        # deal with that.
+    # Test critic pass
+    value_out = actor.critic_pass([sample_obs], [], memories=memories)
+    for stream in stream_names:
+        if lstm:
+            assert value_out[stream].shape == (network_settings.memory.sequence_length,)
+        else:
+            assert value_out[stream].shape == (1,)
+
+    # Test get_dist_and_value
+    dists, value_out, _ = actor.get_dist_and_value([sample_obs], [], memories=memories)
+    for dist in dists:
+        assert isinstance(dist, GaussianDistInstance)
+    for stream in stream_names:
+        if lstm:
+            assert value_out[stream].shape == (network_settings.memory.sequence_length,)
+        else:
+            assert value_out[stream].shape == (1,)

ml-agents/mlagents/trainers/tests/torch/test_encoders.py

+import torch
+from unittest import mock
+import pytest
+
+from mlagents.trainers.torch.encoders import (
+    VectorEncoder,
+    VectorAndUnnormalizedInputEncoder,
+    Normalizer,
+    SimpleVisualEncoder,
+    ResNetVisualEncoder,
+    NatureVisualEncoder,
+)
+
+
+# This test will also reveal issues with states not being saved in the state_dict.
+def compare_models(module_1, module_2):
+    is_same = True
+    for key_item_1, key_item_2 in zip(
+        module_1.state_dict().items(), module_2.state_dict().items()
+    ):
+        # Compare tensors in state_dict and not the keys.
+        is_same = torch.equal(key_item_1[1], key_item_2[1]) and is_same
+    return is_same
+
+
+def test_normalizer():
+    input_size = 2
+    norm = Normalizer(input_size)
+
+    # These three inputs should mean to 0.5, and variance 2
+    # with the steps starting at 1
+    vec_input1 = torch.tensor([[1, 1]])
+    vec_input2 = torch.tensor([[1, 1]])
+    vec_input3 = torch.tensor([[0, 0]])
+    norm.update(vec_input1)
+    norm.update(vec_input2)
+    norm.update(vec_input3)
+
+    # Test normalization
+    for val in norm(vec_input1)[0]:
+        assert val == pytest.approx(0.707, abs=0.001)
+
+    # Test copy normalization
+    norm2 = Normalizer(input_size)
+    assert not compare_models(norm, norm2)
+    norm2.copy_from(norm)
+    assert compare_models(norm, norm2)
+    for val in norm2(vec_input1)[0]:
+        assert val == pytest.approx(0.707, abs=0.001)
+
+
+@mock.patch("mlagents.trainers.torch.encoders.Normalizer")
+def test_vector_encoder(mock_normalizer):
+    mock_normalizer_inst = mock.Mock()
+    mock_normalizer.return_value = mock_normalizer_inst
+    input_size = 64
+    hidden_size = 128
+    num_layers = 3
+    normalize = False
+    vector_encoder = VectorEncoder(input_size, hidden_size, num_layers, normalize)
+    output = vector_encoder(torch.ones((1, input_size)))
+    assert output.shape == (1, hidden_size)
+
+    normalize = True
+    vector_encoder = VectorEncoder(input_size, hidden_size, num_layers, normalize)
+    new_vec = torch.ones((1, input_size))
+    vector_encoder.update_normalization(new_vec)
+
+    mock_normalizer.assert_called_with(input_size)
+    mock_normalizer_inst.update.assert_called_with(new_vec)
+
+    vector_encoder2 = VectorEncoder(input_size, hidden_size, num_layers, normalize)
+    vector_encoder.copy_normalization(vector_encoder2)
+    mock_normalizer_inst.copy_from.assert_called_with(mock_normalizer_inst)
+
+
+@mock.patch("mlagents.trainers.torch.encoders.Normalizer")
+def test_vector_and_unnormalized_encoder(mock_normalizer):
+    mock_normalizer_inst = mock.Mock()
+    mock_normalizer.return_value = mock_normalizer_inst
+    input_size = 64
+    unnormalized_size = 32
+    hidden_size = 128
+    num_layers = 3
+    normalize = True
+    mock_normalizer_inst.return_value = torch.ones((1, input_size))
+    vector_encoder = VectorAndUnnormalizedInputEncoder(
+        input_size, hidden_size, unnormalized_size, num_layers, normalize
+    )
+    # Make sure normalizer is only called on input_size
+    mock_normalizer.assert_called_with(input_size)
+    normal_input = torch.ones((1, input_size))
+
+    unnormalized_input = torch.ones((1, 32))
+    output = vector_encoder(normal_input, unnormalized_input)
+    mock_normalizer_inst.assert_called_with(normal_input)
+    assert output.shape == (1, hidden_size)
+
+
+@pytest.mark.parametrize("image_size", [(36, 36, 3), (84, 84, 4), (256, 256, 5)])
+@pytest.mark.parametrize(
+    "vis_class", [SimpleVisualEncoder, ResNetVisualEncoder, NatureVisualEncoder]
+)
+def test_visual_encoder(vis_class, image_size):
+    num_outputs = 128
+    enc = vis_class(image_size[0], image_size[1], image_size[2], num_outputs)
+    # Note: NCHW not NHWC
+    sample_input = torch.ones((1, image_size[2], image_size[0], image_size[1]))
+    encoding = enc(sample_input)
+    assert encoding.shape == (1, num_outputs)

ml-agents/mlagents/trainers/tests/torch/test_decoders.py

+import pytest
+import torch
+
+from mlagents.trainers.torch.decoders import ValueHeads
+
+
+def test_valueheads():
+    stream_names = [f"reward_signal_{num}" for num in range(5)]
+    input_size = 5
+    batch_size = 4
+
+    # Test default 1 value per head
+    value_heads = ValueHeads(stream_names, input_size)
+    input_data = torch.ones((batch_size, input_size))
+    value_out = value_heads(input_data)  # Note: mean value will be removed shortly
+
+    for stream_name in stream_names:
+        assert value_out[stream_name].shape == (batch_size,)
+
+    # Test that inputting the wrong size input will throw an error
+    with pytest.raises(Exception):
+        value_out = value_heads(torch.ones((batch_size, input_size + 2)))
+
+    # Test multiple values per head (e.g. discrete Q function)
+    output_size = 4
+    value_heads = ValueHeads(stream_names, input_size, output_size)
+    input_data = torch.ones((batch_size, input_size))
+    value_out = value_heads(input_data)
+
+    for stream_name in stream_names:
+        assert value_out[stream_name].shape == (batch_size, output_size)

ml-agents/mlagents/trainers/tests/torch/test_distributions.py

+import pytest
+import torch
+
+from mlagents.trainers.torch.distributions import (
+    GaussianDistribution,
+    MultiCategoricalDistribution,
+    GaussianDistInstance,
+    TanhGaussianDistInstance,
+    CategoricalDistInstance,
+)
+
+
+@pytest.mark.parametrize("tanh_squash", [True, False])
+@pytest.mark.parametrize("conditional_sigma", [True, False])
+def test_gaussian_distribution(conditional_sigma, tanh_squash):
+    torch.manual_seed(0)
+    hidden_size = 16
+    act_size = 4
+    sample_embedding = torch.ones((1, 16))
+    gauss_dist = GaussianDistribution(
+        hidden_size,
+        act_size,
+        conditional_sigma=conditional_sigma,
+        tanh_squash=tanh_squash,
+    )
+
+    # Make sure backprop works
+    force_action = torch.zeros((1, act_size))
+    optimizer = torch.optim.Adam(gauss_dist.parameters(), lr=3e-3)
+
+    for _ in range(50):
+        dist_inst = gauss_dist(sample_embedding)[0]
+        if tanh_squash:
+            assert isinstance(dist_inst, TanhGaussianDistInstance)
+        else:
+            assert isinstance(dist_inst, GaussianDistInstance)
+        log_prob = dist_inst.log_prob(force_action)
+        loss = torch.nn.functional.mse_loss(log_prob, -2 * torch.ones(log_prob.shape))
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    for prob in log_prob.flatten():
+        assert prob == pytest.approx(-2, abs=0.1)
+
+
+def test_multi_categorical_distribution():
+    torch.manual_seed(0)
+    hidden_size = 16
+    act_size = [3, 3, 4]
+    sample_embedding = torch.ones((1, 16))
+    gauss_dist = MultiCategoricalDistribution(hidden_size, act_size)
+
+    # Make sure backprop works
+    optimizer = torch.optim.Adam(gauss_dist.parameters(), lr=3e-3)
+
+    def create_test_prob(size: int) -> torch.Tensor:
+        test_prob = torch.tensor(
+            [[1.0 - 0.01 * (size - 1)] + [0.01] * (size - 1)]
+        )  # High prob for first action
+        return test_prob.log()
+
+    for _ in range(100):
+        dist_insts = gauss_dist(sample_embedding, masks=torch.ones((1, sum(act_size))))
+        loss = 0
+        for i, dist_inst in enumerate(dist_insts):
+            assert isinstance(dist_inst, CategoricalDistInstance)
+            log_prob = dist_inst.all_log_prob()
+            test_log_prob = create_test_prob(act_size[i])
+            # Force log_probs to match the high probability for the first action generated by
+            # create_test_prob
+            loss += torch.nn.functional.mse_loss(log_prob, test_log_prob)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+    for dist_inst, size in zip(dist_insts, act_size):
+        # Check that the log probs are close to the fake ones that we generated.
+        test_log_probs = create_test_prob(size)
+        for _prob, _test_prob in zip(
+            dist_inst.all_log_prob().flatten().tolist(),
+            test_log_probs.flatten().tolist(),
+        ):
+            assert _prob == pytest.approx(_test_prob, abs=0.1)
+
+    # Test masks
+    masks = []
+    for branch in act_size:
+        masks += [0] * (branch - 1) + [1]
+    masks = torch.tensor([masks])
+    dist_insts = gauss_dist(sample_embedding, masks=masks)
+    for dist_inst in dist_insts:
+        log_prob = dist_inst.all_log_prob()
+        assert log_prob.flatten()[-1] == pytest.approx(0, abs=0.001)
+
+
+def test_gaussian_dist_instance():
+    torch.manual_seed(0)
+    act_size = 4
+    dist_instance = GaussianDistInstance(
+        torch.zeros(1, act_size), torch.ones(1, act_size)
+    )
+    action = dist_instance.sample()
+    assert action.shape == (1, act_size)
+    for log_prob in dist_instance.log_prob(torch.zeros((1, act_size))).flatten():
+        # Log prob of standard normal at 0
+        assert log_prob == pytest.approx(-0.919, abs=0.01)
+
+    for ent in dist_instance.entropy().flatten():
+        # entropy of standard normal at 0, based on 1/2 + ln(sqrt(2pi)sigma)
+        assert ent == pytest.approx(1.42, abs=0.01)
+
+
+def test_tanh_gaussian_dist_instance():
+    torch.manual_seed(0)
+    act_size = 4
+    dist_instance = TanhGaussianDistInstance(
+        torch.zeros(1, act_size), torch.ones(1, act_size)
+    )
+    for _ in range(10):
+        action = dist_instance.sample()
+        assert action.shape == (1, act_size)
+        assert torch.max(action) < 1.0 and torch.min(action) > -1.0
+
+
+def test_categorical_dist_instance():
+    torch.manual_seed(0)
+    act_size = 4
+    test_prob = torch.tensor(
+        [[1.0 - 0.1 * (act_size - 1)] + [0.1] * (act_size - 1)]
+    )  # High prob for first action
+    dist_instance = CategoricalDistInstance(test_prob)
+
+    for _ in range(10):
+        action = dist_instance.sample()
+        assert action.shape == (1, 1)
+        assert action < act_size
+
+    # Make sure the first action as higher probability than the others.
+    prob_first_action = dist_instance.log_prob(torch.tensor([0]))
+
+    for i in range(1, act_size):
+        assert dist_instance.log_prob(torch.tensor([i])) < prob_first_action

ml-agents/mlagents/trainers/tests/torch/test_utils.py

+import pytest
+import torch
+import numpy as np
+
+from mlagents.trainers.settings import EncoderType, ScheduleType
+from mlagents.trainers.torch.utils import ModelUtils
+from mlagents.trainers.exception import UnityTrainerException
+from mlagents.trainers.torch.encoders import (
+    VectorEncoder,
+    VectorAndUnnormalizedInputEncoder,
+)
+from mlagents.trainers.torch.distributions import (
+    CategoricalDistInstance,
+    GaussianDistInstance,
+)
+
+
+def test_min_visual_size():
+    # Make sure each EncoderType has an entry in MIS_RESOLUTION_FOR_ENCODER
+    assert set(ModelUtils.MIN_RESOLUTION_FOR_ENCODER.keys()) == set(EncoderType)
+
+    for encoder_type in EncoderType:
+        good_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type]
+        vis_input = torch.ones((1, 3, good_size, good_size))
+        ModelUtils._check_resolution_for_encoder(good_size, good_size, encoder_type)
+        enc_func = ModelUtils.get_encoder_for_type(encoder_type)
+        enc = enc_func(good_size, good_size, 3, 1)
+        enc.forward(vis_input)
+
+        # Anything under the min size should raise an exception. If not, decrease the min size!
+        with pytest.raises(Exception):
+            bad_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type] - 1
+            vis_input = torch.ones((1, 3, bad_size, bad_size))
+
+            with pytest.raises(UnityTrainerException):
+                # Make sure we'd hit a friendly error during model setup time.
+                ModelUtils._check_resolution_for_encoder(
+                    bad_size, bad_size, encoder_type
+                )
+
+            enc = enc_func(bad_size, bad_size, 3, 1)
+            enc.forward(vis_input)
+
+
+@pytest.mark.parametrize("unnormalized_inputs", [0, 1])
+@pytest.mark.parametrize("num_visual", [0, 1, 2])
+@pytest.mark.parametrize("num_vector", [0, 1, 2])
+@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize("encoder_type", [EncoderType.SIMPLE, EncoderType.NATURE_CNN])
+def test_create_encoders(
+    encoder_type, normalize, num_vector, num_visual, unnormalized_inputs
+):
+    vec_obs_shape = (5,)
+    vis_obs_shape = (84, 84, 3)
+    obs_shapes = []
+    for _ in range(num_vector):
+        obs_shapes.append(vec_obs_shape)
+    for _ in range(num_visual):
+        obs_shapes.append(vis_obs_shape)
+    h_size = 128
+    num_layers = 3
+    unnormalized_inputs = 1
+    vis_enc, vec_enc = ModelUtils.create_encoders(
+        obs_shapes, h_size, num_layers, encoder_type, unnormalized_inputs, normalize
+    )
+    vec_enc = list(vec_enc)
+    vis_enc = list(vis_enc)
+    assert len(vec_enc) == (
+        1 if unnormalized_inputs + num_vector > 0 else 0
+    )  # There's always at most one vector encoder.
+    assert len(vis_enc) == num_visual
+
+    if unnormalized_inputs > 0:
+        assert isinstance(vec_enc[0], VectorAndUnnormalizedInputEncoder)
+    elif num_vector > 0:
+        assert isinstance(vec_enc[0], VectorEncoder)
+
+    for enc in vis_enc:
+        assert isinstance(enc, ModelUtils.get_encoder_for_type(encoder_type))
+
+
+def test_decayed_value():
+    test_steps = [0, 4, 9]
+    # Test constant decay
+    param = ModelUtils.DecayedValue(ScheduleType.CONSTANT, 1.0, 0.2, test_steps[-1])
+    for _step in test_steps:
+        _param = param.get_value(_step)
+        assert _param == 1.0
+
+    test_results = [1.0, 0.6444, 0.2]
+    # Test linear decay
+    param = ModelUtils.DecayedValue(ScheduleType.LINEAR, 1.0, 0.2, test_steps[-1])
+    for _step, _result in zip(test_steps, test_results):
+        _param = param.get_value(_step)
+        assert _param == pytest.approx(_result, abs=0.01)
+
+    # Test invalid
+    with pytest.raises(UnityTrainerException):
+        ModelUtils.DecayedValue(
+            "SomeOtherSchedule", 1.0, 0.2, test_steps[-1]
+        ).get_value(0)
+
+
+def test_polynomial_decay():
+    test_steps = [0, 4, 9]
+    test_results = [1.0, 0.7, 0.2]
+    for _step, _result in zip(test_steps, test_results):
+        decayed = ModelUtils.polynomial_decay(
+            1.0, 0.2, test_steps[-1], _step, power=0.8
+        )
+        assert decayed == pytest.approx(_result, abs=0.01)
+
+
+def test_list_to_tensor():
+    # Test converting pure list
+    unconverted_list = [[1, 2], [1, 3], [1, 4]]
+    tensor = ModelUtils.list_to_tensor(unconverted_list)
+    # Should be equivalent to torch.tensor conversion
+    assert torch.equal(tensor, torch.tensor(unconverted_list))
+
+    # Test converting pure numpy array
+    np_list = np.asarray(unconverted_list)
+    tensor = ModelUtils.list_to_tensor(np_list)
+    # Should be equivalent to torch.tensor conversion
+    assert torch.equal(tensor, torch.tensor(unconverted_list))
+
+    # Test converting list of numpy arrays
+    list_of_np = [np.asarray(_el) for _el in unconverted_list]
+    tensor = ModelUtils.list_to_tensor(list_of_np)
+    # Should be equivalent to torch.tensor conversion
+    assert torch.equal(tensor, torch.tensor(unconverted_list))
+
+
+def test_break_into_branches():
+    # Test normal multi-branch case
+    all_actions = torch.tensor([[1, 2, 3, 4, 5, 6]])
+    action_size = [2, 1, 3]
+    broken_actions = ModelUtils.break_into_branches(all_actions, action_size)
+    assert len(action_size) == len(broken_actions)
+    for i, _action in enumerate(broken_actions):
+        assert _action.shape == (1, action_size[i])
+
+    # Test 1-branch case
+    action_size = [6]
+    broken_actions = ModelUtils.break_into_branches(all_actions, action_size)
+    assert len(broken_actions) == 1
+    assert broken_actions[0].shape == (1, 6)
+
+
+def test_actions_to_onehot():
+    all_actions = torch.tensor([[1, 0, 2], [1, 0, 2]])
+    action_size = [2, 1, 3]
+    oh_actions = ModelUtils.actions_to_onehot(all_actions, action_size)
+    expected_result = [
+        torch.tensor([[0, 1], [0, 1]], dtype=torch.float),
+        torch.tensor([[1], [1]], dtype=torch.float),
+        torch.tensor([[0, 0, 1], [0, 0, 1]], dtype=torch.float),
+    ]
+    for res, exp in zip(oh_actions, expected_result):
+        assert torch.equal(res, exp)
+
+
+def test_get_probs_and_entropy():
+    # Test continuous
+    # Add two dists to the list. This isn't done in the code but we'd like to support it.
+    dist_list = [
+        GaussianDistInstance(torch.zeros((1, 2)), torch.ones((1, 2))),
+        GaussianDistInstance(torch.zeros((1, 2)), torch.ones((1, 2))),
+    ]
+    action_list = [torch.zeros((1, 2)), torch.zeros((1, 2))]
+    log_probs, entropies, all_probs = ModelUtils.get_probs_and_entropy(
+        action_list, dist_list
+    )
+    assert log_probs.shape == (1, 2, 2)
+    assert entropies.shape == (1, 2, 2)
+    assert all_probs is None
+
+    for log_prob in log_probs.flatten():
+        # Log prob of standard normal at 0
+        assert log_prob == pytest.approx(-0.919, abs=0.01)
+
+    for ent in entropies.flatten():
+        # entropy of standard normal at 0
+        assert ent == pytest.approx(1.42, abs=0.01)
+
+    # Test continuous
+    # Add two dists to the list.
+    act_size = 2
+    test_prob = torch.tensor(
+        [[1.0 - 0.1 * (act_size - 1)] + [0.1] * (act_size - 1)]
+    )  # High prob for first action
+    dist_list = [CategoricalDistInstance(test_prob), CategoricalDistInstance(test_prob)]
+    action_list = [torch.tensor([0]), torch.tensor([1])]
+    log_probs, entropies, all_probs = ModelUtils.get_probs_and_entropy(
+        action_list, dist_list
+    )
+    assert all_probs.shape == (1, len(dist_list * act_size))
+    assert entropies.shape == (1, len(dist_list))
+    # Make sure the first action has high probability than the others.
+    assert log_probs.flatten()[0] > log_probs.flatten()[1]

ml-agents/mlagents/trainers/tests/torch/test_layers.py

+import torch
+
+from mlagents.trainers.torch.layers import Swish, linear_layer, Initialization
+
+
+def test_swish():
+    layer = Swish()
+    input_tensor = torch.Tensor([[1, 2, 3], [4, 5, 6]])
+    target_tensor = torch.mul(input_tensor, torch.sigmoid(input_tensor))
+    assert torch.all(torch.eq(layer(input_tensor), target_tensor))
+
+
+def test_initialization_layer():
+    torch.manual_seed(0)
+    # Test Zero
+    layer = linear_layer(
+        3, 4, kernel_init=Initialization.Zero, bias_init=Initialization.Zero
+    )
+    assert torch.all(torch.eq(layer.weight.data, torch.zeros_like(layer.weight.data)))
+    assert torch.all(torch.eq(layer.bias.data, torch.zeros_like(layer.bias.data)))

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_curiosity.py

+import numpy as np
+import pytest
+import torch
+from mlagents.trainers.torch.components.reward_providers import (
+    CuriosityRewardProvider,
+    create_reward_provider,
+)
+from mlagents_envs.base_env import BehaviorSpec, ActionType
+from mlagents.trainers.settings import CuriositySettings, RewardSignalType
+from mlagents.trainers.tests.torch.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+
+SEED = [42]
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3)),
+    ],
+)
+def test_construction(behavior_spec: BehaviorSpec) -> None:
+    curiosity_settings = CuriositySettings(32, 0.01)
+    curiosity_settings.strength = 0.1
+    curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
+    assert curiosity_rp.strength == 0.1
+    assert curiosity_rp.name == "Curiosity"
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,), (64, 66, 3), (84, 86, 1)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,), (64, 66, 1)], ActionType.DISCRETE, (2, 3)),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2,)),
+    ],
+)
+def test_factory(behavior_spec: BehaviorSpec) -> None:
+    curiosity_settings = CuriositySettings(32, 0.01)
+    curiosity_rp = create_reward_provider(
+        RewardSignalType.CURIOSITY, behavior_spec, curiosity_settings
+    )
+    assert curiosity_rp.name == "Curiosity"
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,), (64, 66, 3), (24, 26, 1)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3)),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2,)),
+    ],
+)
+def test_reward_decreases(behavior_spec: BehaviorSpec, seed: int) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    curiosity_settings = CuriositySettings(32, 0.01)
+    curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
+    buffer = create_agent_buffer(behavior_spec, 5)
+    curiosity_rp.update(buffer)
+    reward_old = curiosity_rp.evaluate(buffer)[0]
+    for _ in range(10):
+        curiosity_rp.update(buffer)
+        reward_new = curiosity_rp.evaluate(buffer)[0]
+        assert reward_new < reward_old
+        reward_old = reward_new
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec", [BehaviorSpec([(10,)], ActionType.CONTINUOUS, 5)]
+)
+def test_continuous_action_prediction(behavior_spec: BehaviorSpec, seed: int) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    curiosity_settings = CuriositySettings(32, 0.1)
+    curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
+    buffer = create_agent_buffer(behavior_spec, 5)
+    for _ in range(200):
+        curiosity_rp.update(buffer)
+    prediction = curiosity_rp._network.predict_action(buffer)[0].detach()
+    target = buffer["actions"][0]
+    error = float(torch.mean((prediction - target) ** 2))
+    assert error < 0.001
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,), (64, 66, 3)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3)),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2,)),
+    ],
+)
+def test_next_state_prediction(behavior_spec: BehaviorSpec, seed: int) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    curiosity_settings = CuriositySettings(32, 0.1)
+    curiosity_rp = CuriosityRewardProvider(behavior_spec, curiosity_settings)
+    buffer = create_agent_buffer(behavior_spec, 5)
+    for _ in range(100):
+        curiosity_rp.update(buffer)
+    prediction = curiosity_rp._network.predict_next_state(buffer)[0]
+    target = curiosity_rp._network.get_next_state(buffer)[0]
+    error = float(torch.mean((prediction - target) ** 2).detach())
+    assert error < 0.001

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_extrinsic.py

+import pytest
+from mlagents.trainers.torch.components.reward_providers import (
+    ExtrinsicRewardProvider,
+    create_reward_provider,
+)
+from mlagents_envs.base_env import BehaviorSpec, ActionType
+from mlagents.trainers.settings import RewardSignalSettings, RewardSignalType
+from mlagents.trainers.tests.torch.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3)),
+    ],
+)
+def test_construction(behavior_spec: BehaviorSpec) -> None:
+    settings = RewardSignalSettings()
+    settings.gamma = 0.2
+    extrinsic_rp = ExtrinsicRewardProvider(behavior_spec, settings)
+    assert extrinsic_rp.gamma == 0.2
+    assert extrinsic_rp.name == "Extrinsic"
+
+
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3)),
+    ],
+)
+def test_factory(behavior_spec: BehaviorSpec) -> None:
+    settings = RewardSignalSettings()
+    extrinsic_rp = create_reward_provider(
+        RewardSignalType.EXTRINSIC, behavior_spec, settings
+    )
+    assert extrinsic_rp.name == "Extrinsic"
+
+
+@pytest.mark.parametrize("reward", [2.0, 3.0, 4.0])
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(10,)], ActionType.CONTINUOUS, 5),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3)),
+    ],
+)
+def test_reward(behavior_spec: BehaviorSpec, reward: float) -> None:
+    buffer = create_agent_buffer(behavior_spec, 1000, reward)
+    settings = RewardSignalSettings()
+    extrinsic_rp = ExtrinsicRewardProvider(behavior_spec, settings)
+    generated_rewards = extrinsic_rp.evaluate(buffer)
+    assert (generated_rewards == reward).all()

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_gail.py

+from typing import Any
+import numpy as np
+import pytest
+from unittest.mock import patch
+import torch
+import os
+from mlagents.trainers.torch.components.reward_providers import (
+    GAILRewardProvider,
+    create_reward_provider,
+)
+from mlagents_envs.base_env import BehaviorSpec, ActionType
+from mlagents.trainers.settings import GAILSettings, RewardSignalType
+from mlagents.trainers.tests.torch.test_reward_providers.utils import (
+    create_agent_buffer,
+)
+from mlagents.trainers.torch.components.reward_providers.gail_reward_provider import (
+    DiscriminatorNetwork,
+)
+
+CONTINUOUS_PATH = (
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir, os.pardir)
+    + "/test.demo"
+)
+DISCRETE_PATH = (
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir, os.pardir)
+    + "/testdcvis.demo"
+)
+SEED = [42]
+
+
+@pytest.mark.parametrize(
+    "behavior_spec", [BehaviorSpec([(8,)], ActionType.CONTINUOUS, 2)]
+)
+def test_construction(behavior_spec: BehaviorSpec) -> None:
+    gail_settings = GAILSettings(demo_path=CONTINUOUS_PATH)
+    gail_rp = GAILRewardProvider(behavior_spec, gail_settings)
+    assert gail_rp.name == "GAIL"
+
+
+@pytest.mark.parametrize(
+    "behavior_spec", [BehaviorSpec([(8,)], ActionType.CONTINUOUS, 2)]
+)
+def test_factory(behavior_spec: BehaviorSpec) -> None:
+    gail_settings = GAILSettings(demo_path=CONTINUOUS_PATH)
+    gail_rp = create_reward_provider(
+        RewardSignalType.GAIL, behavior_spec, gail_settings
+    )
+    assert gail_rp.name == "GAIL"
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(8,), (24, 26, 1)], ActionType.CONTINUOUS, 2),
+        BehaviorSpec([(50,)], ActionType.DISCRETE, (2, 3, 3, 3)),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (20,)),
+    ],
+)
+@pytest.mark.parametrize("use_actions", [False, True])
+@patch(
+    "mlagents.trainers.torch.components.reward_providers.gail_reward_provider.demo_to_buffer"
+)
+def test_reward_decreases(
+    demo_to_buffer: Any, use_actions: bool, behavior_spec: BehaviorSpec, seed: int
+) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    buffer_expert = create_agent_buffer(behavior_spec, 1000)
+    buffer_policy = create_agent_buffer(behavior_spec, 1000)
+    demo_to_buffer.return_value = None, buffer_expert
+    gail_settings = GAILSettings(
+        demo_path="", learning_rate=0.05, use_vail=False, use_actions=use_actions
+    )
+    gail_rp = create_reward_provider(
+        RewardSignalType.GAIL, behavior_spec, gail_settings
+    )
+
+    init_reward_expert = gail_rp.evaluate(buffer_expert)[0]
+    init_reward_policy = gail_rp.evaluate(buffer_policy)[0]
+
+    for _ in range(10):
+        gail_rp.update(buffer_policy)
+        reward_expert = gail_rp.evaluate(buffer_expert)[0]
+        reward_policy = gail_rp.evaluate(buffer_policy)[0]
+        assert reward_expert >= 0  # GAIL / VAIL reward always positive
+        assert reward_policy >= 0
+    reward_expert = gail_rp.evaluate(buffer_expert)[0]
+    reward_policy = gail_rp.evaluate(buffer_policy)[0]
+    assert reward_expert > reward_policy  # Expert reward greater than non-expert reward
+    assert (
+        reward_expert > init_reward_expert
+    )  # Expert reward getting better as network trains
+    assert (
+        reward_policy < init_reward_policy
+    )  # Non-expert reward getting worse as network trains
+
+
+@pytest.mark.parametrize("seed", SEED)
+@pytest.mark.parametrize(
+    "behavior_spec",
+    [
+        BehaviorSpec([(8,)], ActionType.CONTINUOUS, 2),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (2, 3, 3, 3)),
+        BehaviorSpec([(10,)], ActionType.DISCRETE, (20,)),
+    ],
+)
+@pytest.mark.parametrize("use_actions", [False, True])
+@patch(
+    "mlagents.trainers.torch.components.reward_providers.gail_reward_provider.demo_to_buffer"
+)
+def test_reward_decreases_vail(
+    demo_to_buffer: Any, use_actions: bool, behavior_spec: BehaviorSpec, seed: int
+) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    buffer_expert = create_agent_buffer(behavior_spec, 1000)
+    buffer_policy = create_agent_buffer(behavior_spec, 1000)
+    demo_to_buffer.return_value = None, buffer_expert
+    gail_settings = GAILSettings(
+        demo_path="", learning_rate=0.005, use_vail=True, use_actions=use_actions
+    )
+    DiscriminatorNetwork.initial_beta = 0.0
+    # we must set the initial value of beta to 0 for testing
+    # If we do not, the kl-loss will dominate early and will block the estimator
+    gail_rp = create_reward_provider(
+        RewardSignalType.GAIL, behavior_spec, gail_settings
+    )
+
+    for _ in range(100):
+        gail_rp.update(buffer_policy)
+        reward_expert = gail_rp.evaluate(buffer_expert)[0]
+        reward_policy = gail_rp.evaluate(buffer_policy)[0]
+        assert reward_expert >= 0  # GAIL / VAIL reward always positive
+        assert reward_policy >= 0
+    reward_expert = gail_rp.evaluate(buffer_expert)[0]
+    reward_policy = gail_rp.evaluate(buffer_policy)[0]
+    assert reward_expert > reward_policy  # Expert reward greater than non-expert reward

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/utils.py

+import numpy as np
+from mlagents.trainers.buffer import AgentBuffer
+from mlagents_envs.base_env import BehaviorSpec
+from mlagents.trainers.trajectory import SplitObservations
+
+
+def create_agent_buffer(
+    behavior_spec: BehaviorSpec, number: int, reward: float = 0.0
+) -> AgentBuffer:
+    buffer = AgentBuffer()
+    curr_observations = [
+        np.random.normal(size=shape) for shape in behavior_spec.observation_shapes
+    ]
+    next_observations = [
+        np.random.normal(size=shape) for shape in behavior_spec.observation_shapes
+    ]
+    action = behavior_spec.create_random_action(1)[0, :]
+    for _ in range(number):
+        curr_split_obs = SplitObservations.from_observations(curr_observations)
+        next_split_obs = SplitObservations.from_observations(next_observations)
+        for i, _ in enumerate(curr_split_obs.visual_observations):
+            buffer["visual_obs%d" % i].append(curr_split_obs.visual_observations[i])
+            buffer["next_visual_obs%d" % i].append(
+                next_split_obs.visual_observations[i]
+            )
+        buffer["vector_obs"].append(curr_split_obs.vector_observations)
+        buffer["next_vector_in"].append(next_split_obs.vector_observations)
+        buffer["actions"].append(action)
+        buffer["done"].append(np.zeros(1, dtype=np.float32))
+        buffer["reward"].append(np.ones(1, dtype=np.float32) * reward)
+        buffer["masks"].append(np.ones(1, dtype=np.float32))
+    return buffer

ml-agents/mlagents/trainers/tests/torch/testdcvis.demo

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ml-agents/mlagents/trainers/tests/torch/test_bcmodule.py

+from unittest.mock import MagicMock
+import pytest
+import mlagents.trainers.tests.mock_brain as mb
+
+import numpy as np
+import os
+
+from mlagents.trainers.policy.torch_policy import TorchPolicy
+from mlagents.trainers.torch.components.bc.module import BCModule
+from mlagents.trainers.settings import (
+    TrainerSettings,
+    BehavioralCloningSettings,
+    NetworkSettings,
+)
+
+
+def create_bc_module(mock_behavior_specs, bc_settings, use_rnn, tanhresample):
+    # model_path = env.external_brain_names[0]
+    trainer_config = TrainerSettings()
+    trainer_config.network_settings.memory = (
+        NetworkSettings.MemorySettings() if use_rnn else None
+    )
+    policy = TorchPolicy(
+        0,
+        mock_behavior_specs,
+        trainer_config,
+        "test",
+        False,
+        tanhresample,
+        tanhresample,
+    )
+    bc_module = BCModule(
+        policy,
+        settings=bc_settings,
+        policy_learning_rate=trainer_config.hyperparameters.learning_rate,
+        default_batch_size=trainer_config.hyperparameters.batch_size,
+        default_num_epoch=3,
+    )
+    return bc_module
+
+
+# Test default values
+def test_bcmodule_defaults():
+    # See if default values match
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, False)
+    assert bc_module.num_epoch == 3
+    assert bc_module.batch_size == TrainerSettings().hyperparameters.batch_size
+    # Assign strange values and see if it overrides properly
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo",
+        num_epoch=100,
+        batch_size=10000,
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, False)
+    assert bc_module.num_epoch == 100
+    assert bc_module.batch_size == 10000
+
+
+# Test with continuous control env and vector actions
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_update(is_sac):
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
+    stats = bc_module.update()
+    for _, item in stats.items():
+        assert isinstance(item, np.float32)
+
+
+# Test with constant pretraining learning rate
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_constant_lr_update(is_sac):
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo",
+        steps=0,
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
+    stats = bc_module.update()
+    for _, item in stats.items():
+        assert isinstance(item, np.float32)
+    old_learning_rate = bc_module.current_lr
+
+    _ = bc_module.update()
+    assert old_learning_rate == bc_module.current_lr
+
+
+# Test with constant pretraining learning rate
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_linear_lr_update(is_sac):
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo",
+        steps=100,
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
+    # Should decay by 10/100 * 0.0003 = 0.00003
+    bc_module.policy.get_current_step = MagicMock(return_value=10)
+    old_learning_rate = bc_module.current_lr
+    _ = bc_module.update()
+    assert old_learning_rate - 0.00003 == pytest.approx(bc_module.current_lr, abs=0.01)
+
+
+# Test with RNN
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_rnn_update(is_sac):
+    mock_specs = mb.create_mock_3dball_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "test.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, True, is_sac)
+    stats = bc_module.update()
+    for _, item in stats.items():
+        assert isinstance(item, np.float32)
+
+
+# Test with discrete control and visual observations
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_dc_visual_update(is_sac):
+    mock_specs = mb.create_mock_banana_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "testdcvis.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, False, is_sac)
+    stats = bc_module.update()
+    for _, item in stats.items():
+        assert isinstance(item, np.float32)
+
+
+# Test with discrete control, visual observations and RNN
+@pytest.mark.parametrize("is_sac", [True, False], ids=["sac", "ppo"])
+def test_bcmodule_rnn_dc_update(is_sac):
+    mock_specs = mb.create_mock_banana_behavior_specs()
+    bc_settings = BehavioralCloningSettings(
+        demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" + "testdcvis.demo"
+    )
+    bc_module = create_bc_module(mock_specs, bc_settings, True, is_sac)
+    stats = bc_module.update()
+    for _, item in stats.items():
+        assert isinstance(item, np.float32)
+
+
+if __name__ == "__main__":
+    pytest.main()

Project/Assets/ML-Agents/Examples/SharedAssets/Scripts/OrientationCubeController.cs.meta

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Project/Assets/csc.rsp

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com.unity.ml-agents/Runtime/SensorHelper.cs.meta

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utils/validate_release_links.py

-#!/usr/bin/env python3
-
-import ast
-import sys
-import os
-import re
-import subprocess
-from typing import List, Optional, Pattern
-
-RELEASE_PATTERN = re.compile(r"release_[0-9]+(_docs)*")
-TRAINER_INIT_FILE = "ml-agents/mlagents/trainers/__init__.py"
-
-# Filename -> regex list to allow specific lines.
-# To allow everything in the file, use None for the value
-ALLOW_LIST = {
-    # Previous release table
-    "README.md": re.compile(r"\*\*Release [0-9]+\*\*"),
-    "docs/Versioning.md": None,
-    "com.unity.ml-agents/CHANGELOG.md": None,
-    "utils/make_readme_table.py": None,
-    "utils/validate_release_links.py": None,
-}
-
-
-def test_pattern():
-    # Just some sanity check that the regex works as expected.
-    assert RELEASE_PATTERN.search(
-        "https://github.com/Unity-Technologies/ml-agents/blob/release_4_docs/Food.md"
-    )
-    assert RELEASE_PATTERN.search(
-        "https://github.com/Unity-Technologies/ml-agents/blob/release_4/Foo.md"
-    )
-    assert RELEASE_PATTERN.search(
-        "git clone --branch release_4 https://github.com/Unity-Technologies/ml-agents.git"
-    )
-    assert RELEASE_PATTERN.search(
-        "https://github.com/Unity-Technologies/ml-agents/blob/release_123_docs/Foo.md"
-    )
-    assert RELEASE_PATTERN.search(
-        "https://github.com/Unity-Technologies/ml-agents/blob/release_123/Foo.md"
-    )
-    assert not RELEASE_PATTERN.search(
-        "https://github.com/Unity-Technologies/ml-agents/blob/latest_release/docs/Foo.md"
-    )
-    print("tests OK!")
-
-
-def git_ls_files() -> List[str]:
-    """
-    Run "git ls-files" and return a list with one entry per line.
-    This returns the list of all files tracked by git.
-    """
-    return subprocess.check_output(["git", "ls-files"], universal_newlines=True).split(
-        "\n"
-    )
-
-
-def get_release_tag() -> Optional[str]:
-    """
-    Returns the release tag for the mlagents python package.
-    This will be None on the master branch.
-    :return:
-    """
-    with open(TRAINER_INIT_FILE) as f:
-        for line in f:
-            if "__release_tag__" in line:
-                lhs, equals_string, rhs = line.strip().partition(" = ")
-                # Evaluate the right hand side of the expression
-                return ast.literal_eval(rhs)
-    # If we couldn't find the release tag, raise an exception
-    # (since we can't return None here)
-    raise RuntimeError("Can't determine release tag")
-
-
-def check_file(filename: str, global_allow_pattern: Pattern) -> List[str]:
-    """
-    Validate a single file and return any offending lines.
-    """
-    bad_lines = []
-    with open(filename) as f:
-        for line in f:
-            if not RELEASE_PATTERN.search(line):
-                continue
-
-            if global_allow_pattern.search(line):
-                continue
-
-            if filename in ALLOW_LIST:
-                if ALLOW_LIST[filename] is None or ALLOW_LIST[filename].search(line):
-                    continue
-
-            bad_lines.append(f"{filename}: {line.strip()}")
-    return bad_lines
-
-
-def check_all_files(allow_pattern: Pattern) -> List[str]:
-    """
-    Validate all files tracked by git.
-    :param allow_pattern:
-    """
-    bad_lines = []
-    file_types = {".py", ".md", ".cs"}
-    for file_name in git_ls_files():
-        if "localized" in file_name or os.path.splitext(file_name)[1] not in file_types:
-            continue
-        bad_lines += check_file(file_name, allow_pattern)
-    return bad_lines
-
-
-def main():
-    release_tag = get_release_tag()
-    if not release_tag:
-        print("Release tag is None, exiting")
-        sys.exit(0)
-
-    print(f"Release tag: {release_tag}")
-    allow_pattern = re.compile(f"{release_tag}(_docs)*")
-    bad_lines = check_all_files(allow_pattern)
-    if bad_lines:
-        print(
-            f"Found lines referring to previous release. Either update the files, or add an exclusion to {__file__}"
-        )
-        for line in bad_lines:
-            print(line)
-
-    sys.exit(1 if bad_lines else 0)
-
-
-if __name__ == "__main__":
-    if "--test" in sys.argv:
-        test_pattern()
-    main()

ml-agents/mlagents/trainers/tests/test_models.py

-import pytest
-
-from mlagents.trainers.tf.models import ModelUtils
-from mlagents.tf_utils import tf
-from mlagents_envs.base_env import BehaviorSpec, ActionType
-
-
-def create_behavior_spec(num_visual, num_vector, vector_size):
-    behavior_spec = BehaviorSpec(
-        [(84, 84, 3)] * int(num_visual) + [(vector_size,)] * int(num_vector),
-        ActionType.DISCRETE,
-        (1,),
-    )
-    return behavior_spec
-
-
-@pytest.mark.parametrize("num_visual", [1, 2, 4])
-@pytest.mark.parametrize("num_vector", [1, 2, 4])
-def test_create_input_placeholders(num_vector, num_visual):
-    vec_size = 8
-    name_prefix = "test123"
-    bspec = create_behavior_spec(num_visual, num_vector, vec_size)
-    vec_in, vis_in = ModelUtils.create_input_placeholders(
-        bspec.observation_shapes, name_prefix=name_prefix
-    )
-
-    assert isinstance(vis_in, list)
-    assert len(vis_in) == num_visual
-    assert isinstance(vec_in, tf.Tensor)
-    assert vec_in.get_shape().as_list()[1] == num_vector * 8
-
-    # Check names contain prefix and vis shapes are correct
-    for _vis in vis_in:
-        assert _vis.get_shape().as_list() == [None, 84, 84, 3]
-        assert _vis.name.startswith(name_prefix)
-    assert vec_in.name.startswith(name_prefix)