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Merge pull request #4707 from Unity-Technologies/develop-rm-tf
Merge pull request #4707 from Unity-Technologies/develop-rm-tf
Removing TensorFlow Trainers/MLA-1734-demo-provider
GitHub
4 年前
当前提交
903d3afe
共有 42 个文件被更改，包括 113 次插入 和 2783 次删除
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13.github/workflows/pytest.yml
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1com.unity.ml-agents/CHANGELOG.md
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4docs/ML-Agents-Overview.md
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2docs/Training-Configuration-File.md
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3docs/Training-ML-Agents.md
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17docs/Unity-Inference-Engine.md
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2ml-agents-envs/setup.py
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25ml-agents/mlagents/trainers/cli_utils.py
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7ml-agents/mlagents/trainers/learn.py
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75ml-agents/mlagents/trainers/ppo/trainer.py
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106ml-agents/mlagents/trainers/sac/trainer.py
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10ml-agents/mlagents/trainers/settings.py
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2ml-agents/mlagents/trainers/stats.py
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2ml-agents/mlagents/trainers/tests/__init__.py
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17ml-agents/mlagents/trainers/tests/test_rl_trainer.py
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19ml-agents/mlagents/trainers/tests/test_trainer_controller.py
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4ml-agents/mlagents/trainers/tests/test_training_status.py
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6ml-agents/mlagents/trainers/tests/torch/test_ghost.py
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6ml-agents/mlagents/trainers/tests/torch/test_hybrid.py
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9ml-agents/mlagents/trainers/tests/torch/test_ppo.py
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5ml-agents/mlagents/trainers/tests/torch/test_sac.py
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5ml-agents/mlagents/trainers/tests/torch/test_simple_rl.py
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56ml-agents/mlagents/trainers/trainer/rl_trainer.py
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24ml-agents/mlagents/trainers/trainer/trainer_factory.py
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14ml-agents/mlagents/trainers/trainer_controller.py
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6ml-agents/mlagents/trainers/training_status.py
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1ml-agents/setup.py
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11ml-agents/tests/yamato/yamato_utils.py
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4test_requirements.txt
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175ml-agents/mlagents/trainers/model_saver/tf_model_saver.py
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168ml-agents/mlagents/trainers/optimizer/tf_optimizer.py
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630ml-agents/mlagents/trainers/policy/tf_policy.py
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361ml-agents/mlagents/trainers/ppo/optimizer_tf.py
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444ml-agents/mlagents/trainers/sac/network.py
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641ml-agents/mlagents/trainers/sac/optimizer_tf.py
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8test_constraints_min_version.txt
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6test_constraints_max_tf2_version.txt
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7test_constraints_max_tf1_version.txt
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0/ml-agents/mlagents/torch_utils/globals.py
|
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import os |
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import shutil |
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from typing import Optional, Union, cast |
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from mlagents_envs.exception import UnityPolicyException |
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from mlagents_envs.logging_util import get_logger |
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from mlagents.tf_utils import tf |
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from mlagents.trainers.model_saver.model_saver import BaseModelSaver |
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from mlagents.trainers.tf.model_serialization import export_policy_model |
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from mlagents.trainers.settings import TrainerSettings, SerializationSettings |
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from mlagents.trainers.policy.tf_policy import TFPolicy |
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from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer |
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from mlagents.trainers import __version__ |
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|
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logger = get_logger(__name__) |
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|
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class TFModelSaver(BaseModelSaver): |
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""" |
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ModelSaver class for TensorFlow |
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""" |
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|
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def __init__( |
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self, trainer_settings: TrainerSettings, model_path: str, load: bool = False |
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): |
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super().__init__() |
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self.model_path = model_path |
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self.initialize_path = trainer_settings.init_path |
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self._keep_checkpoints = trainer_settings.keep_checkpoints |
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self.load = load |
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# Currently only support saving one policy. This is the one to be saved. |
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self.policy: Optional[TFPolicy] = None |
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self.graph = None |
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self.sess = None |
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self.tf_saver = None |
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def register(self, module: Union[TFPolicy, TFOptimizer]) -> None: |
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if isinstance(module, TFPolicy): |
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self._register_policy(module) |
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elif isinstance(module, TFOptimizer): |
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self._register_optimizer(module) |
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else: |
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raise UnityPolicyException( |
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"Registering Object of unsupported type {} to Saver ".format( |
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type(module) |
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) |
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) |
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|
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def _register_policy(self, policy: TFPolicy) -> None: |
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if self.policy is None: |
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self.policy = policy |
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self.graph = self.policy.graph |
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self.sess = self.policy.sess |
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with self.policy.graph.as_default(): |
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self.tf_saver = tf.train.Saver(max_to_keep=self._keep_checkpoints) |
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def save_checkpoint(self, behavior_name: str, step: int) -> str: |
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checkpoint_path = os.path.join(self.model_path, f"{behavior_name}-{step}") |
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# Save the TF checkpoint and graph definition |
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if self.graph: |
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with self.graph.as_default(): |
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if self.tf_saver: |
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self.tf_saver.save(self.sess, f"{checkpoint_path}.ckpt") |
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tf.train.write_graph( |
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self.graph, self.model_path, "raw_graph_def.pb", as_text=False |
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) |
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# also save the policy so we have optimized model files for each checkpoint |
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self.export(checkpoint_path, behavior_name) |
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return checkpoint_path |
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def export(self, output_filepath: str, behavior_name: str) -> None: |
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# save model if there is only one worker or |
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# only on worker-0 if there are multiple workers |
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if self.policy and self.policy.rank is not None and self.policy.rank != 0: |
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return |
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if self.graph is None: |
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logger.info("No model to export") |
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return |
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export_policy_model( |
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self.model_path, output_filepath, behavior_name, self.graph, self.sess |
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) |
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|
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def initialize_or_load(self, policy: Optional[TFPolicy] = None) -> None: |
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# If there is an initialize path, load from that. Else, load from the set model path. |
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# If load is set to True, don't reset steps to 0. Else, do. This allows a user to, |
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# e.g., resume from an initialize path. |
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if policy is None: |
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policy = self.policy |
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policy = cast(TFPolicy, policy) |
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reset_steps = not self.load |
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if self.initialize_path is not None: |
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self._load_graph( |
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policy, self.initialize_path, reset_global_steps=reset_steps |
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) |
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elif self.load: |
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self._load_graph(policy, self.model_path, reset_global_steps=reset_steps) |
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else: |
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policy.initialize() |
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TFPolicy.broadcast_global_variables(0) |
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def _load_graph( |
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self, policy: TFPolicy, model_path: str, reset_global_steps: bool = False |
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) -> None: |
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# This prevents normalizer init up from executing on load |
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policy.first_normalization_update = False |
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with policy.graph.as_default(): |
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logger.info(f"Loading model from {model_path}.") |
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ckpt = tf.train.get_checkpoint_state(model_path) |
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if ckpt is None: |
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raise UnityPolicyException( |
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"The model {} could not be loaded. Make " |
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"sure you specified the right " |
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"--run-id and that the previous run you are loading from had the same " |
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"behavior names.".format(model_path) |
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) |
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if self.tf_saver: |
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try: |
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self.tf_saver.restore(policy.sess, ckpt.model_checkpoint_path) |
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except tf.errors.NotFoundError: |
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raise UnityPolicyException( |
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"The model {} was found but could not be loaded. Make " |
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"sure the model is from the same version of ML-Agents, has the same behavior parameters, " |
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"and is using the same trainer configuration as the current run.".format( |
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model_path |
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) |
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) |
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self._check_model_version(__version__) |
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if reset_global_steps: |
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policy.set_step(0) |
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logger.info( |
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"Starting training from step 0 and saving to {}.".format( |
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self.model_path |
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) |
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) |
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else: |
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logger.info(f"Resuming training from step {policy.get_current_step()}.") |
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def _check_model_version(self, version: str) -> None: |
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""" |
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Checks whether the model being loaded was created with the same version of |
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ML-Agents, and throw a warning if not so. |
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""" |
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if self.policy is not None and self.policy.version_tensors is not None: |
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loaded_ver = tuple( |
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num.eval(session=self.sess) for num in self.policy.version_tensors |
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) |
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if loaded_ver != TFPolicy._convert_version_string(version): |
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logger.warning( |
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f"The model checkpoint you are loading from was saved with ML-Agents version " |
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f"{loaded_ver[0]}.{loaded_ver[1]}.{loaded_ver[2]} but your current ML-Agents" |
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f"version is {version}. Model may not behave properly." |
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) |
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def copy_final_model(self, source_nn_path: str) -> None: |
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""" |
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Copy the .nn file at the given source to the destination. |
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Also copies the corresponding .onnx file if it exists. |
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""" |
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final_model_name = os.path.splitext(source_nn_path)[0] |
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|
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if SerializationSettings.convert_to_barracuda: |
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source_path = f"{final_model_name}.nn" |
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destination_path = f"{self.model_path}.nn" |
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shutil.copyfile(source_path, destination_path) |
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logger.info(f"Copied {source_path} to {destination_path}.") |
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if SerializationSettings.convert_to_onnx: |
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try: |
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source_path = f"{final_model_name}.onnx" |
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destination_path = f"{self.model_path}.onnx" |
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shutil.copyfile(source_path, destination_path) |
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logger.info(f"Copied {source_path} to {destination_path}.") |
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except OSError: |
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pass |
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from typing import Dict, Any, List, Tuple, Optional |
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import numpy as np |
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from mlagents.tf_utils.tf import tf |
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from mlagents.trainers.buffer import AgentBuffer |
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from mlagents.trainers.policy.tf_policy import TFPolicy |
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from mlagents.trainers.optimizer import Optimizer |
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from mlagents.trainers.trajectory import SplitObservations |
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from mlagents.trainers.tf.components.reward_signals.reward_signal_factory import ( |
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create_reward_signal, |
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) |
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from mlagents.trainers.settings import TrainerSettings, RewardSignalType |
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from mlagents.trainers.tf.components.bc.module import BCModule |
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class TFOptimizer(Optimizer): # pylint: disable=W0223 |
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def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings): |
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super().__init__() |
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self.sess = policy.sess |
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self.policy = policy |
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self.update_dict: Dict[str, tf.Tensor] = {} |
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self.value_heads: Dict[str, tf.Tensor] = {} |
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self.create_reward_signals(trainer_params.reward_signals) |
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self.memory_in: tf.Tensor = None |
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self.memory_out: tf.Tensor = None |
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self.m_size: int = 0 |
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self.bc_module: Optional[BCModule] = None |
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# Create pretrainer if needed |
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if trainer_params.behavioral_cloning is not None: |
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self.bc_module = BCModule( |
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self.policy, |
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trainer_params.behavioral_cloning, |
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policy_learning_rate=trainer_params.hyperparameters.learning_rate, |
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default_batch_size=trainer_params.hyperparameters.batch_size, |
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default_num_epoch=3, |
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) |
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|
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def get_trajectory_value_estimates( |
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self, batch: AgentBuffer, next_obs: List[np.ndarray], done: bool |
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) -> Tuple[Dict[str, np.ndarray], Dict[str, float]]: |
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feed_dict: Dict[tf.Tensor, Any] = { |
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self.policy.batch_size_ph: batch.num_experiences, |
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self.policy.sequence_length_ph: batch.num_experiences, # We want to feed data in batch-wise, not time-wise. |
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} |
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if self.policy.vec_obs_size > 0: |
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feed_dict[self.policy.vector_in] = batch["vector_obs"] |
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if self.policy.vis_obs_size > 0: |
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for i in range(len(self.policy.visual_in)): |
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_obs = batch["visual_obs%d" % i] |
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feed_dict[self.policy.visual_in[i]] = _obs |
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if self.policy.use_recurrent: |
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feed_dict[self.policy.memory_in] = [ |
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np.zeros((self.policy.m_size), dtype=np.float32) |
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] |
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feed_dict[self.memory_in] = [np.zeros((self.m_size), dtype=np.float32)] |
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if self.policy.prev_action is not None: |
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feed_dict[self.policy.prev_action] = batch["prev_action"] |
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|
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if self.policy.use_recurrent: |
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value_estimates, policy_mem, value_mem = self.sess.run( |
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[self.value_heads, self.policy.memory_out, self.memory_out], feed_dict |
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) |
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prev_action = ( |
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batch["discrete_action"][-1] |
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if not self.policy.use_continuous_act |
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else None |
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) |
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else: |
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value_estimates = self.sess.run(self.value_heads, feed_dict) |
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prev_action = None |
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policy_mem = None |
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value_mem = None |
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value_estimates = {k: np.squeeze(v, axis=1) for k, v in value_estimates.items()} |
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# We do this in a separate step to feed the memory outs - a further optimization would |
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# be to append to the obs before running sess.run. |
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final_value_estimates = self._get_value_estimates( |
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next_obs, done, policy_mem, value_mem, prev_action |
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) |
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return value_estimates, final_value_estimates |
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|
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def _get_value_estimates( |
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self, |
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next_obs: List[np.ndarray], |
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done: bool, |
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policy_memory: np.ndarray = None, |
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value_memory: np.ndarray = None, |
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prev_action: np.ndarray = None, |
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) -> Dict[str, float]: |
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""" |
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Generates value estimates for bootstrapping. |
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:param experience: AgentExperience to be used for bootstrapping. |
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:param done: Whether or not this is the last element of the episode, in which case the value estimate will be 0. |
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:return: The value estimate dictionary with key being the name of the reward signal and the value the |
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corresponding value estimate. |
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""" |
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|
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feed_dict: Dict[tf.Tensor, Any] = { |
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self.policy.batch_size_ph: 1, |
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self.policy.sequence_length_ph: 1, |
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} |
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vec_vis_obs = SplitObservations.from_observations(next_obs) |
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for i in range(len(vec_vis_obs.visual_observations)): |
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feed_dict[self.policy.visual_in[i]] = [vec_vis_obs.visual_observations[i]] |
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|
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if self.policy.vec_obs_size > 0: |
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feed_dict[self.policy.vector_in] = [vec_vis_obs.vector_observations] |
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if policy_memory is not None: |
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feed_dict[self.policy.memory_in] = policy_memory |
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if value_memory is not None: |
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feed_dict[self.memory_in] = value_memory |
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if prev_action is not None: |
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feed_dict[self.policy.prev_action] = [prev_action] |
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value_estimates = self.sess.run(self.value_heads, feed_dict) |
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value_estimates = {k: float(v) for k, v in value_estimates.items()} |
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|
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# If we're done, reassign all of the value estimates that need terminal states. |
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if done: |
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for k in value_estimates: |
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if self.reward_signals[k].use_terminal_states: |
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value_estimates[k] = 0.0 |
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|
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return value_estimates |
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|
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def create_reward_signals( |
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self, reward_signal_configs: Dict[RewardSignalType, Any] |
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) -> None: |
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""" |
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Create reward signals |
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:param reward_signal_configs: Reward signal config. |
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""" |
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# Create reward signals |
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for reward_signal, settings in reward_signal_configs.items(): |
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# Name reward signals by string in case we have duplicates later |
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self.reward_signals[reward_signal.value] = create_reward_signal( |
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self.policy, reward_signal, settings |
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) |
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self.update_dict.update( |
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self.reward_signals[reward_signal.value].update_dict |
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) |
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|
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@classmethod |
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def create_optimizer_op( |
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cls, learning_rate: tf.Tensor, name: str = "Adam" |
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) -> tf.train.Optimizer: |
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return tf.train.AdamOptimizer(learning_rate=learning_rate, name=name) |
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|
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def _execute_model( |
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self, feed_dict: Dict[tf.Tensor, np.ndarray], out_dict: Dict[str, tf.Tensor] |
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) -> Dict[str, np.ndarray]: |
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""" |
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Executes model. |
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:param feed_dict: Input dictionary mapping nodes to input data. |
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:param out_dict: Output dictionary mapping names to nodes. |
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:return: Dictionary mapping names to input data. |
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""" |
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network_out = self.sess.run(list(out_dict.values()), feed_dict=feed_dict) |
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run_out = dict(zip(list(out_dict.keys()), network_out)) |
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return run_out |
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|
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def _make_zero_mem(self, m_size: int, length: int) -> List[np.ndarray]: |
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return [ |
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np.zeros((m_size), dtype=np.float32) |
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for i in range(0, length, self.policy.sequence_length) |
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] |
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|
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from typing import Any, Dict, List, Optional, Tuple, Callable |
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import numpy as np |
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from distutils.version import LooseVersion |
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|
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from mlagents_envs.timers import timed |
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|
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from mlagents.tf_utils import tf |
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from mlagents import tf_utils |
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from mlagents_envs.exception import UnityException |
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from mlagents_envs.logging_util import get_logger |
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from mlagents.trainers.policy import Policy |
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from mlagents.trainers.action_info import ActionInfo |
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from mlagents.trainers.trajectory import SplitObservations |
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from mlagents.trainers.torch.action_log_probs import LogProbsTuple |
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from mlagents.trainers.behavior_id_utils import get_global_agent_id |
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from mlagents_envs.base_env import DecisionSteps, ActionTuple, BehaviorSpec |
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from mlagents.trainers.tf.models import ModelUtils |
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from mlagents.trainers.settings import TrainerSettings, EncoderType |
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from mlagents.trainers import __version__ |
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from mlagents.trainers.tf.distributions import ( |
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GaussianDistribution, |
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MultiCategoricalDistribution, |
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) |
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from mlagents.tf_utils.globals import get_rank |
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|
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|
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logger = get_logger(__name__) |
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|
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|
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# This is the version number of the inputs and outputs of the model, and |
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# determines compatibility with inference in Barracuda. |
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MODEL_FORMAT_VERSION = 2 |
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|
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EPSILON = 1e-6 # Small value to avoid divide by zero |
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|
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|
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class UnityPolicyException(UnityException): |
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""" |
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Related to errors with the Trainer. |
|||
""" |
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|
|||
pass |
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|
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|
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class TFPolicy(Policy): |
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""" |
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Contains a learning model, and the necessary |
|||
functions to save/load models and create the input placeholders. |
|||
""" |
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|
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# Callback function used at the start of training to synchronize weights. |
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# By default, this nothing. |
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# If this needs to be used, it should be done from outside ml-agents. |
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broadcast_global_variables: Callable[[int], None] = lambda root_rank: None |
|||
|
|||
def __init__( |
|||
self, |
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seed: int, |
|||
behavior_spec: BehaviorSpec, |
|||
trainer_settings: TrainerSettings, |
|||
tanh_squash: bool = False, |
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reparameterize: bool = False, |
|||
condition_sigma_on_obs: bool = True, |
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create_tf_graph: bool = True, |
|||
): |
|||
""" |
|||
Initialized the policy. |
|||
:param seed: Random seed to use for TensorFlow. |
|||
:param brain: The corresponding Brain for this policy. |
|||
:param trainer_settings: The trainer parameters. |
|||
""" |
|||
super().__init__( |
|||
seed, |
|||
behavior_spec, |
|||
trainer_settings, |
|||
tanh_squash, |
|||
reparameterize, |
|||
condition_sigma_on_obs, |
|||
) |
|||
if ( |
|||
self.behavior_spec.action_spec.continuous_size > 0 |
|||
and self.behavior_spec.action_spec.discrete_size > 0 |
|||
): |
|||
raise UnityPolicyException( |
|||
"TensorFlow does not support continuous and discrete actions on the same behavior. " |
|||
"Please run with the Torch framework." |
|||
) |
|||
# for ghost trainer save/load snapshots |
|||
self.assign_phs: List[tf.Tensor] = [] |
|||
self.assign_ops: List[tf.Operation] = [] |
|||
self.update_dict: Dict[str, tf.Tensor] = {} |
|||
self.inference_dict: Dict[str, tf.Tensor] = {} |
|||
self.first_normalization_update: bool = False |
|||
|
|||
self.graph = tf.Graph() |
|||
self.sess = tf.Session( |
|||
config=tf_utils.generate_session_config(), graph=self.graph |
|||
) |
|||
self._initialize_tensorflow_references() |
|||
self.grads = None |
|||
self.update_batch: Optional[tf.Operation] = None |
|||
self.trainable_variables: List[tf.Variable] = [] |
|||
self.rank = get_rank() |
|||
if create_tf_graph: |
|||
self.create_tf_graph() |
|||
|
|||
def get_trainable_variables(self) -> List[tf.Variable]: |
|||
""" |
|||
Returns a List of the trainable variables in this policy. if create_tf_graph hasn't been called, |
|||
returns empty list. |
|||
""" |
|||
return self.trainable_variables |
|||
|
|||
def create_tf_graph(self) -> None: |
|||
""" |
|||
Builds the tensorflow graph needed for this policy. |
|||
""" |
|||
with self.graph.as_default(): |
|||
tf.set_random_seed(self.seed) |
|||
_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) |
|||
if len(_vars) > 0: |
|||
# We assume the first thing created in the graph is the Policy. If |
|||
# already populated, don't create more tensors. |
|||
return |
|||
|
|||
self.create_input_placeholders() |
|||
encoded = self._create_encoder( |
|||
self.visual_in, |
|||
self.processed_vector_in, |
|||
self.h_size, |
|||
self.num_layers, |
|||
self.vis_encode_type, |
|||
) |
|||
if self.use_continuous_act: |
|||
self._create_cc_actor( |
|||
encoded, |
|||
self.tanh_squash, |
|||
self.reparameterize, |
|||
self.condition_sigma_on_obs, |
|||
) |
|||
else: |
|||
self._create_dc_actor(encoded) |
|||
self.trainable_variables = tf.get_collection( |
|||
tf.GraphKeys.TRAINABLE_VARIABLES, scope="policy" |
|||
) |
|||
self.trainable_variables += tf.get_collection( |
|||
tf.GraphKeys.TRAINABLE_VARIABLES, scope="lstm" |
|||
) # LSTMs need to be root scope for Barracuda export |
|||
|
|||
self.inference_dict = { |
|||
"action": self.output, |
|||
"log_probs": self.all_log_probs, |
|||
"entropy": self.entropy, |
|||
} |
|||
if self.use_continuous_act: |
|||
self.inference_dict["pre_action"] = self.output_pre |
|||
if self.use_recurrent: |
|||
self.inference_dict["memory_out"] = self.memory_out |
|||
|
|||
# We do an initialize to make the Policy usable out of the box. If an optimizer is needed, |
|||
# it will re-load the full graph |
|||
self.initialize() |
|||
# Create assignment ops for Ghost Trainer |
|||
self.init_load_weights() |
|||
|
|||
def _create_encoder( |
|||
self, |
|||
visual_in: List[tf.Tensor], |
|||
vector_in: tf.Tensor, |
|||
h_size: int, |
|||
num_layers: int, |
|||
vis_encode_type: EncoderType, |
|||
) -> tf.Tensor: |
|||
""" |
|||
Creates an encoder for visual and vector observations. |
|||
:param h_size: Size of hidden linear layers. |
|||
:param num_layers: Number of hidden linear layers. |
|||
:param vis_encode_type: Type of visual encoder to use if visual input. |
|||
:return: The hidden layer (tf.Tensor) after the encoder. |
|||
""" |
|||
with tf.variable_scope("policy"): |
|||
encoded = ModelUtils.create_observation_streams( |
|||
self.visual_in, |
|||
self.processed_vector_in, |
|||
1, |
|||
h_size, |
|||
num_layers, |
|||
vis_encode_type, |
|||
)[0] |
|||
return encoded |
|||
|
|||
@staticmethod |
|||
def _convert_version_string(version_string: str) -> Tuple[int, ...]: |
|||
""" |
|||
Converts the version string into a Tuple of ints (major_ver, minor_ver, patch_ver). |
|||
:param version_string: The semantic-versioned version string (X.Y.Z). |
|||
:return: A Tuple containing (major_ver, minor_ver, patch_ver). |
|||
""" |
|||
ver = LooseVersion(version_string) |
|||
return tuple(map(int, ver.version[0:3])) |
|||
|
|||
def initialize(self): |
|||
with self.graph.as_default(): |
|||
init = tf.global_variables_initializer() |
|||
self.sess.run(init) |
|||
|
|||
def get_weights(self): |
|||
with self.graph.as_default(): |
|||
_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) |
|||
values = [v.eval(session=self.sess) for v in _vars] |
|||
return values |
|||
|
|||
def init_load_weights(self): |
|||
with self.graph.as_default(): |
|||
_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) |
|||
values = [v.eval(session=self.sess) for v in _vars] |
|||
for var, value in zip(_vars, values): |
|||
assign_ph = tf.placeholder(var.dtype, shape=value.shape) |
|||
self.assign_phs.append(assign_ph) |
|||
self.assign_ops.append(tf.assign(var, assign_ph)) |
|||
|
|||
def load_weights(self, values): |
|||
if len(self.assign_ops) == 0: |
|||
logger.warning( |
|||
"Calling load_weights in tf_policy but assign_ops is empty. Did you forget to call init_load_weights?" |
|||
) |
|||
with self.graph.as_default(): |
|||
feed_dict = {} |
|||
for assign_ph, value in zip(self.assign_phs, values): |
|||
feed_dict[assign_ph] = value |
|||
self.sess.run(self.assign_ops, feed_dict=feed_dict) |
|||
|
|||
@timed |
|||
def evaluate( |
|||
self, decision_requests: DecisionSteps, global_agent_ids: List[str] |
|||
) -> Dict[str, Any]: |
|||
""" |
|||
Evaluates policy for the agent experiences provided. |
|||
:param decision_requests: DecisionSteps object containing inputs. |
|||
:param global_agent_ids: The global (with worker ID) agent ids of the data in the batched_step_result. |
|||
:return: Outputs from network as defined by self.inference_dict. |
|||
""" |
|||
feed_dict = { |
|||
self.batch_size_ph: len(decision_requests), |
|||
self.sequence_length_ph: 1, |
|||
} |
|||
if self.use_recurrent: |
|||
if not self.use_continuous_act: |
|||
feed_dict[self.prev_action] = self.retrieve_previous_action( |
|||
global_agent_ids |
|||
) |
|||
|
|||
feed_dict[self.memory_in] = self.retrieve_memories(global_agent_ids) |
|||
feed_dict = self.fill_eval_dict(feed_dict, decision_requests) |
|||
run_out = self._execute_model(feed_dict, self.inference_dict) |
|||
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 decision_requests: A dictionary of brain names and DecisionSteps from environment. |
|||
:param worker_id: In parallel environment training, the unique id of the environment worker that |
|||
the DecisionSteps came from. Used to construct a globally unique id for each agent. |
|||
: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( # pylint: disable=assignment-from-no-return |
|||
decision_requests, global_agent_ids |
|||
) |
|||
|
|||
self.save_memories(global_agent_ids, run_out.get("memory_out")) |
|||
# For Compatibility with buffer changes for hybrid action support |
|||
if "log_probs" in run_out: |
|||
log_probs_tuple = LogProbsTuple() |
|||
if self.behavior_spec.action_spec.is_continuous(): |
|||
log_probs_tuple.add_continuous(run_out["log_probs"]) |
|||
else: |
|||
log_probs_tuple.add_discrete(run_out["log_probs"]) |
|||
run_out["log_probs"] = log_probs_tuple |
|||
if "action" in run_out: |
|||
action_tuple = ActionTuple() |
|||
env_action_tuple = ActionTuple() |
|||
if self.behavior_spec.action_spec.is_continuous(): |
|||
action_tuple.add_continuous(run_out["pre_action"]) |
|||
env_action_tuple.add_continuous(run_out["action"]) |
|||
else: |
|||
action_tuple.add_discrete(run_out["action"]) |
|||
env_action_tuple.add_discrete(run_out["action"]) |
|||
run_out["action"] = action_tuple |
|||
run_out["env_action"] = env_action_tuple |
|||
self.check_nan_action(run_out.get("action")) |
|||
return ActionInfo( |
|||
action=run_out.get("action"), |
|||
env_action=run_out.get("env_action"), |
|||
value=run_out.get("value"), |
|||
outputs=run_out, |
|||
agent_ids=decision_requests.agent_id, |
|||
) |
|||
|
|||
def update(self, mini_batch, num_sequences): |
|||
""" |
|||
Performs update of the policy. |
|||
:param num_sequences: Number of experience trajectories in batch. |
|||
:param mini_batch: Batch of experiences. |
|||
:return: Results of update. |
|||
""" |
|||
raise UnityPolicyException("The update function was not implemented.") |
|||
|
|||
def _execute_model(self, feed_dict, out_dict): |
|||
""" |
|||
Executes model. |
|||
:param feed_dict: Input dictionary mapping nodes to input data. |
|||
:param out_dict: Output dictionary mapping names to nodes. |
|||
:return: Dictionary mapping names to input data. |
|||
""" |
|||
network_out = self.sess.run(list(out_dict.values()), feed_dict=feed_dict) |
|||
run_out = dict(zip(list(out_dict.keys()), network_out)) |
|||
return run_out |
|||
|
|||
def fill_eval_dict(self, feed_dict, batched_step_result): |
|||
vec_vis_obs = SplitObservations.from_observations(batched_step_result.obs) |
|||
for i, _ in enumerate(vec_vis_obs.visual_observations): |
|||
feed_dict[self.visual_in[i]] = vec_vis_obs.visual_observations[i] |
|||
if self.use_vec_obs: |
|||
feed_dict[self.vector_in] = vec_vis_obs.vector_observations |
|||
if not self.use_continuous_act: |
|||
mask = np.ones( |
|||
( |
|||
len(batched_step_result), |
|||
sum(self.behavior_spec.action_spec.discrete_branches), |
|||
), |
|||
dtype=np.float32, |
|||
) |
|||
if batched_step_result.action_mask is not None: |
|||
mask = 1 - np.concatenate(batched_step_result.action_mask, axis=1) |
|||
feed_dict[self.action_masks] = mask |
|||
return feed_dict |
|||
|
|||
def get_current_step(self): |
|||
""" |
|||
Gets current model step. |
|||
:return: current model step. |
|||
""" |
|||
step = self.sess.run(self.global_step) |
|||
return step |
|||
|
|||
def set_step(self, step: int) -> int: |
|||
""" |
|||
Sets current model step to step without creating additional ops. |
|||
:param step: Step to set the current model step to. |
|||
:return: The step the model was set to. |
|||
""" |
|||
current_step = self.get_current_step() |
|||
# Increment a positive or negative number of steps. |
|||
return self.increment_step(step - current_step) |
|||
|
|||
def increment_step(self, n_steps): |
|||
""" |
|||
Increments model step. |
|||
""" |
|||
out_dict = { |
|||
"global_step": self.global_step, |
|||
"increment_step": self.increment_step_op, |
|||
} |
|||
feed_dict = {self.steps_to_increment: n_steps} |
|||
return self.sess.run(out_dict, feed_dict=feed_dict)["global_step"] |
|||
|
|||
def get_inference_vars(self): |
|||
""" |
|||
:return:list of inference var names |
|||
""" |
|||
return list(self.inference_dict.keys()) |
|||
|
|||
def get_update_vars(self): |
|||
""" |
|||
:return:list of update var names |
|||
""" |
|||
return list(self.update_dict.keys()) |
|||
|
|||
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. |
|||
""" |
|||
if self.use_vec_obs and self.normalize: |
|||
if self.first_normalization_update: |
|||
self.sess.run( |
|||
self.init_normalization_op, feed_dict={self.vector_in: vector_obs} |
|||
) |
|||
self.first_normalization_update = False |
|||
else: |
|||
self.sess.run( |
|||
self.update_normalization_op, feed_dict={self.vector_in: vector_obs} |
|||
) |
|||
|
|||
@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 _initialize_tensorflow_references(self): |
|||
self.value_heads: Dict[str, tf.Tensor] = {} |
|||
self.normalization_steps: Optional[tf.Variable] = None |
|||
self.running_mean: Optional[tf.Variable] = None |
|||
self.running_variance: Optional[tf.Variable] = None |
|||
self.init_normalization_op: Optional[tf.Operation] = None |
|||
self.update_normalization_op: Optional[tf.Operation] = None |
|||
self.value: Optional[tf.Tensor] = None |
|||
self.all_log_probs: tf.Tensor = None |
|||
self.total_log_probs: Optional[tf.Tensor] = None |
|||
self.entropy: Optional[tf.Tensor] = None |
|||
self.output_pre: Optional[tf.Tensor] = None |
|||
self.output: Optional[tf.Tensor] = None |
|||
self.selected_actions: tf.Tensor = None |
|||
self.action_masks: Optional[tf.Tensor] = None |
|||
self.prev_action: Optional[tf.Tensor] = None |
|||
self.memory_in: Optional[tf.Tensor] = None |
|||
self.memory_out: Optional[tf.Tensor] = None |
|||
self.version_tensors: Optional[Tuple[tf.Tensor, tf.Tensor, tf.Tensor]] = None |
|||
|
|||
def create_input_placeholders(self): |
|||
with self.graph.as_default(): |
|||
( |
|||
self.global_step, |
|||
self.increment_step_op, |
|||
self.steps_to_increment, |
|||
) = ModelUtils.create_global_steps() |
|||
self.vector_in, self.visual_in = ModelUtils.create_input_placeholders( |
|||
self.behavior_spec.observation_shapes |
|||
) |
|||
if self.normalize: |
|||
self.first_normalization_update = True |
|||
normalization_tensors = ModelUtils.create_normalizer(self.vector_in) |
|||
self.update_normalization_op = normalization_tensors.update_op |
|||
self.init_normalization_op = normalization_tensors.init_op |
|||
self.normalization_steps = normalization_tensors.steps |
|||
self.running_mean = normalization_tensors.running_mean |
|||
self.running_variance = normalization_tensors.running_variance |
|||
self.processed_vector_in = ModelUtils.normalize_vector_obs( |
|||
self.vector_in, |
|||
self.running_mean, |
|||
self.running_variance, |
|||
self.normalization_steps, |
|||
) |
|||
else: |
|||
self.processed_vector_in = self.vector_in |
|||
self.update_normalization_op = None |
|||
|
|||
self.batch_size_ph = tf.placeholder( |
|||
shape=None, dtype=tf.int32, name="batch_size" |
|||
) |
|||
self.sequence_length_ph = tf.placeholder( |
|||
shape=None, dtype=tf.int32, name="sequence_length" |
|||
) |
|||
self.mask_input = tf.placeholder( |
|||
shape=[None], dtype=tf.float32, name="masks" |
|||
) |
|||
# Only needed for PPO, but needed for BC module |
|||
self.epsilon = tf.placeholder( |
|||
shape=[None, self.act_size[0]], dtype=tf.float32, name="epsilon" |
|||
) |
|||
self.mask = tf.cast(self.mask_input, tf.int32) |
|||
|
|||
tf.Variable( |
|||
int(self.behavior_spec.action_spec.is_continuous()), |
|||
name="is_continuous_control", |
|||
trainable=False, |
|||
dtype=tf.int32, |
|||
) |
|||
int_version = TFPolicy._convert_version_string(__version__) |
|||
major_ver_t = tf.Variable( |
|||
int_version[0], |
|||
name="trainer_major_version", |
|||
trainable=False, |
|||
dtype=tf.int32, |
|||
) |
|||
minor_ver_t = tf.Variable( |
|||
int_version[1], |
|||
name="trainer_minor_version", |
|||
trainable=False, |
|||
dtype=tf.int32, |
|||
) |
|||
patch_ver_t = tf.Variable( |
|||
int_version[2], |
|||
name="trainer_patch_version", |
|||
trainable=False, |
|||
dtype=tf.int32, |
|||
) |
|||
self.version_tensors = (major_ver_t, minor_ver_t, patch_ver_t) |
|||
tf.Variable( |
|||
MODEL_FORMAT_VERSION, |
|||
name="version_number", |
|||
trainable=False, |
|||
dtype=tf.int32, |
|||
) |
|||
tf.Variable( |
|||
self.m_size, name="memory_size", trainable=False, dtype=tf.int32 |
|||
) |
|||
if self.behavior_spec.action_spec.is_continuous(): |
|||
tf.Variable( |
|||
self.act_size[0], |
|||
name="action_output_shape", |
|||
trainable=False, |
|||
dtype=tf.int32, |
|||
) |
|||
else: |
|||
tf.Variable( |
|||
sum(self.act_size), |
|||
name="action_output_shape", |
|||
trainable=False, |
|||
dtype=tf.int32, |
|||
) |
|||
|
|||
def _create_cc_actor( |
|||
self, |
|||
encoded: tf.Tensor, |
|||
tanh_squash: bool = False, |
|||
reparameterize: bool = False, |
|||
condition_sigma_on_obs: bool = True, |
|||
) -> None: |
|||
""" |
|||
Creates Continuous control actor-critic model. |
|||
:param h_size: Size of hidden linear layers. |
|||
:param num_layers: Number of hidden linear layers. |
|||
:param vis_encode_type: Type of visual encoder to use if visual input. |
|||
:param tanh_squash: Whether to use a tanh function, or a clipped output. |
|||
:param reparameterize: Whether we are using the resampling trick to update the policy. |
|||
""" |
|||
if self.use_recurrent: |
|||
self.memory_in = tf.placeholder( |
|||
shape=[None, self.m_size], dtype=tf.float32, name="recurrent_in" |
|||
) |
|||
hidden_policy, memory_policy_out = ModelUtils.create_recurrent_encoder( |
|||
encoded, self.memory_in, self.sequence_length_ph, name="lstm_policy" |
|||
) |
|||
|
|||
self.memory_out = tf.identity(memory_policy_out, name="recurrent_out") |
|||
else: |
|||
hidden_policy = encoded |
|||
|
|||
with tf.variable_scope("policy"): |
|||
distribution = GaussianDistribution( |
|||
hidden_policy, |
|||
self.act_size, |
|||
reparameterize=reparameterize, |
|||
tanh_squash=tanh_squash, |
|||
condition_sigma=condition_sigma_on_obs, |
|||
) |
|||
|
|||
if tanh_squash: |
|||
self.output_pre = distribution.sample |
|||
self.output = tf.identity(self.output_pre, name="action") |
|||
else: |
|||
self.output_pre = distribution.sample |
|||
# Clip and scale output to ensure actions are always within [-1, 1] range. |
|||
output_post = tf.clip_by_value(self.output_pre, -3, 3) / 3 |
|||
self.output = tf.identity(output_post, name="action") |
|||
|
|||
self.selected_actions = tf.stop_gradient(self.output) |
|||
|
|||
self.all_log_probs = tf.identity(distribution.log_probs, name="action_probs") |
|||
self.entropy = distribution.entropy |
|||
|
|||
# We keep these tensors the same name, but use new nodes to keep code parallelism with discrete control. |
|||
self.total_log_probs = distribution.total_log_probs |
|||
|
|||
def _create_dc_actor(self, encoded: tf.Tensor) -> None: |
|||
""" |
|||
Creates Discrete control actor-critic model. |
|||
:param h_size: Size of hidden linear layers. |
|||
:param num_layers: Number of hidden linear layers. |
|||
:param vis_encode_type: Type of visual encoder to use if visual input. |
|||
""" |
|||
if self.use_recurrent: |
|||
self.prev_action = tf.placeholder( |
|||
shape=[None, len(self.act_size)], dtype=tf.int32, name="prev_action" |
|||
) |
|||
prev_action_oh = tf.concat( |
|||
[ |
|||
tf.one_hot(self.prev_action[:, i], self.act_size[i]) |
|||
for i in range(len(self.act_size)) |
|||
], |
|||
axis=1, |
|||
) |
|||
hidden_policy = tf.concat([encoded, prev_action_oh], axis=1) |
|||
|
|||
self.memory_in = tf.placeholder( |
|||
shape=[None, self.m_size], dtype=tf.float32, name="recurrent_in" |
|||
) |
|||
hidden_policy, memory_policy_out = ModelUtils.create_recurrent_encoder( |
|||
hidden_policy, |
|||
self.memory_in, |
|||
self.sequence_length_ph, |
|||
name="lstm_policy", |
|||
) |
|||
|
|||
self.memory_out = tf.identity(memory_policy_out, "recurrent_out") |
|||
else: |
|||
hidden_policy = encoded |
|||
|
|||
self.action_masks = tf.placeholder( |
|||
shape=[None, sum(self.act_size)], dtype=tf.float32, name="action_masks" |
|||
) |
|||
|
|||
with tf.variable_scope("policy"): |
|||
distribution = MultiCategoricalDistribution( |
|||
hidden_policy, self.act_size, self.action_masks |
|||
) |
|||
# It's important that we are able to feed_dict a value into this tensor to get the |
|||
# right one-hot encoding, so we can't do identity on it. |
|||
self.output = distribution.sample |
|||
self.all_log_probs = tf.identity(distribution.log_probs, name="action") |
|||
self.selected_actions = tf.stop_gradient( |
|||
distribution.sample_onehot |
|||
) # In discrete, these are onehot |
|||
self.entropy = distribution.entropy |
|||
self.total_log_probs = distribution.total_log_probs |
|||
|
|||
from typing import Optional, Any, Dict, cast |
|||
import numpy as np |
|||
from mlagents.tf_utils import tf |
|||
from mlagents_envs.timers import timed |
|||
from mlagents.trainers.tf.models import ModelUtils, EncoderType |
|||
from mlagents.trainers.policy.tf_policy import TFPolicy |
|||
from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer |
|||
from mlagents.trainers.buffer import AgentBuffer |
|||
from mlagents.trainers.settings import TrainerSettings, PPOSettings |
|||
|
|||
|
|||
class PPOOptimizer(TFOptimizer): |
|||
def __init__(self, policy: TFPolicy, trainer_params: 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. |
|||
policy.create_tf_graph() |
|||
|
|||
with policy.graph.as_default(): |
|||
with tf.variable_scope("optimizer/"): |
|||
super().__init__(policy, trainer_params) |
|||
hyperparameters: PPOSettings = cast( |
|||
PPOSettings, trainer_params.hyperparameters |
|||
) |
|||
lr = float(hyperparameters.learning_rate) |
|||
self._schedule = hyperparameters.learning_rate_schedule |
|||
epsilon = float(hyperparameters.epsilon) |
|||
beta = float(hyperparameters.beta) |
|||
max_step = float(trainer_params.max_steps) |
|||
|
|||
policy_network_settings = policy.network_settings |
|||
h_size = int(policy_network_settings.hidden_units) |
|||
num_layers = policy_network_settings.num_layers |
|||
vis_encode_type = policy_network_settings.vis_encode_type |
|||
self.burn_in_ratio = 0.0 |
|||
|
|||
self.stream_names = list(self.reward_signals.keys()) |
|||
|
|||
self.tf_optimizer_op: Optional[tf.train.Optimizer] = None |
|||
self.grads = None |
|||
self.update_batch: Optional[tf.Operation] = None |
|||
|
|||
self.stats_name_to_update_name = { |
|||
"Losses/Value Loss": "value_loss", |
|||
"Losses/Policy Loss": "policy_loss", |
|||
"Policy/Learning Rate": "learning_rate", |
|||
"Policy/Epsilon": "decay_epsilon", |
|||
"Policy/Beta": "decay_beta", |
|||
} |
|||
if self.policy.use_recurrent: |
|||
self.m_size = self.policy.m_size |
|||
self.memory_in = tf.placeholder( |
|||
shape=[None, self.m_size], |
|||
dtype=tf.float32, |
|||
name="recurrent_value_in", |
|||
) |
|||
|
|||
if num_layers < 1: |
|||
num_layers = 1 |
|||
if policy.use_continuous_act: |
|||
self._create_cc_critic(h_size, num_layers, vis_encode_type) |
|||
else: |
|||
self._create_dc_critic(h_size, num_layers, vis_encode_type) |
|||
|
|||
self.learning_rate = ModelUtils.create_schedule( |
|||
self._schedule, |
|||
lr, |
|||
self.policy.global_step, |
|||
int(max_step), |
|||
min_value=1e-10, |
|||
) |
|||
self._create_losses( |
|||
self.policy.total_log_probs, |
|||
self.old_log_probs, |
|||
self.value_heads, |
|||
self.policy.entropy, |
|||
beta, |
|||
epsilon, |
|||
lr, |
|||
max_step, |
|||
) |
|||
self._create_ppo_optimizer_ops() |
|||
|
|||
self.update_dict.update( |
|||
{ |
|||
"value_loss": self.value_loss, |
|||
"policy_loss": self.abs_policy_loss, |
|||
"update_batch": self.update_batch, |
|||
"learning_rate": self.learning_rate, |
|||
"decay_epsilon": self.decay_epsilon, |
|||
"decay_beta": self.decay_beta, |
|||
} |
|||
) |
|||
|
|||
def _create_cc_critic( |
|||
self, h_size: int, num_layers: int, vis_encode_type: EncoderType |
|||
) -> None: |
|||
""" |
|||
Creates Continuous control critic (value) network. |
|||
:param h_size: Size of hidden linear layers. |
|||
:param num_layers: Number of hidden linear layers. |
|||
:param vis_encode_type: The type of visual encoder to use. |
|||
""" |
|||
hidden_stream = ModelUtils.create_observation_streams( |
|||
self.policy.visual_in, |
|||
self.policy.processed_vector_in, |
|||
1, |
|||
h_size, |
|||
num_layers, |
|||
vis_encode_type, |
|||
)[0] |
|||
|
|||
if self.policy.use_recurrent: |
|||
hidden_value, memory_value_out = ModelUtils.create_recurrent_encoder( |
|||
hidden_stream, |
|||
self.memory_in, |
|||
self.policy.sequence_length_ph, |
|||
name="lstm_value", |
|||
) |
|||
self.memory_out = memory_value_out |
|||
else: |
|||
hidden_value = hidden_stream |
|||
|
|||
self.value_heads, self.value = ModelUtils.create_value_heads( |
|||
self.stream_names, hidden_value |
|||
) |
|||
self.all_old_log_probs = tf.placeholder( |
|||
shape=[None, sum(self.policy.act_size)], |
|||
dtype=tf.float32, |
|||
name="old_probabilities", |
|||
) |
|||
|
|||
self.old_log_probs = tf.reduce_sum( |
|||
(tf.identity(self.all_old_log_probs)), axis=1, keepdims=True |
|||
) |
|||
|
|||
def _create_dc_critic( |
|||
self, h_size: int, num_layers: int, vis_encode_type: EncoderType |
|||
) -> None: |
|||
""" |
|||
Creates Discrete control critic (value) network. |
|||
:param h_size: Size of hidden linear layers. |
|||
:param num_layers: Number of hidden linear layers. |
|||
:param vis_encode_type: The type of visual encoder to use. |
|||
""" |
|||
hidden_stream = ModelUtils.create_observation_streams( |
|||
self.policy.visual_in, |
|||
self.policy.processed_vector_in, |
|||
1, |
|||
h_size, |
|||
num_layers, |
|||
vis_encode_type, |
|||
)[0] |
|||
|
|||
if self.policy.use_recurrent: |
|||
hidden_value, memory_value_out = ModelUtils.create_recurrent_encoder( |
|||
hidden_stream, |
|||
self.memory_in, |
|||
self.policy.sequence_length_ph, |
|||
name="lstm_value", |
|||
) |
|||
self.memory_out = memory_value_out |
|||
else: |
|||
hidden_value = hidden_stream |
|||
|
|||
self.value_heads, self.value = ModelUtils.create_value_heads( |
|||
self.stream_names, hidden_value |
|||
) |
|||
|
|||
self.all_old_log_probs = tf.placeholder( |
|||
shape=[None, sum(self.policy.act_size)], |
|||
dtype=tf.float32, |
|||
name="old_probabilities", |
|||
) |
|||
|
|||
# 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 |
|||
) |
|||
|
|||
_, _, old_normalized_logits = ModelUtils.create_discrete_action_masking_layer( |
|||
old_log_prob_branches, self.policy.action_masks, self.policy.act_size |
|||
) |
|||
|
|||
action_idx = [0] + list(np.cumsum(self.policy.act_size)) |
|||
|
|||
self.old_log_probs = tf.reduce_sum( |
|||
( |
|||
tf.stack( |
|||
[ |
|||
-tf.nn.softmax_cross_entropy_with_logits_v2( |
|||
labels=self.policy.selected_actions[ |
|||
:, action_idx[i] : action_idx[i + 1] |
|||
], |
|||
logits=old_normalized_logits[ |
|||
:, action_idx[i] : action_idx[i + 1] |
|||
], |
|||
) |
|||
for i in range(len(self.policy.act_size)) |
|||
], |
|||
axis=1, |
|||
) |
|||
), |
|||
axis=1, |
|||
keepdims=True, |
|||
) |
|||
|
|||
def _create_losses( |
|||
self, probs, old_probs, value_heads, entropy, beta, epsilon, lr, max_step |
|||
): |
|||
""" |
|||
Creates training-specific Tensorflow ops for PPO models. |
|||
:param probs: Current policy probabilities |
|||
:param old_probs: Past policy probabilities |
|||
:param value_heads: Value estimate tensors from each value stream |
|||
:param beta: Entropy regularization strength |
|||
:param entropy: Current policy entropy |
|||
:param epsilon: Value for policy-divergence threshold |
|||
:param lr: Learning rate |
|||
:param max_step: Total number of training steps. |
|||
""" |
|||
self.returns_holders = {} |
|||
self.old_values = {} |
|||
for name in value_heads.keys(): |
|||
returns_holder = tf.placeholder( |
|||
shape=[None], dtype=tf.float32, name=f"{name}_returns" |
|||
) |
|||
old_value = tf.placeholder( |
|||
shape=[None], dtype=tf.float32, name=f"{name}_value_estimate" |
|||
) |
|||
self.returns_holders[name] = returns_holder |
|||
self.old_values[name] = old_value |
|||
self.advantage = tf.placeholder( |
|||
shape=[None], dtype=tf.float32, name="advantages" |
|||
) |
|||
advantage = tf.expand_dims(self.advantage, -1) |
|||
|
|||
self.decay_epsilon = ModelUtils.create_schedule( |
|||
self._schedule, epsilon, self.policy.global_step, max_step, min_value=0.1 |
|||
) |
|||
self.decay_beta = ModelUtils.create_schedule( |
|||
self._schedule, beta, self.policy.global_step, max_step, min_value=1e-5 |
|||
) |
|||
|
|||
value_losses = [] |
|||
for name, head in value_heads.items(): |
|||
clipped_value_estimate = self.old_values[name] + tf.clip_by_value( |
|||
tf.reduce_sum(head, axis=1) - self.old_values[name], |
|||
-self.decay_epsilon, |
|||
self.decay_epsilon, |
|||
) |
|||
v_opt_a = tf.squared_difference( |
|||
self.returns_holders[name], tf.reduce_sum(head, axis=1) |
|||
) |
|||
v_opt_b = tf.squared_difference( |
|||
self.returns_holders[name], clipped_value_estimate |
|||
) |
|||
value_loss = tf.reduce_mean( |
|||
tf.dynamic_partition(tf.maximum(v_opt_a, v_opt_b), self.policy.mask, 2)[ |
|||
1 |
|||
] |
|||
) |
|||
value_losses.append(value_loss) |
|||
self.value_loss = tf.reduce_mean(value_losses) |
|||
|
|||
r_theta = tf.exp(probs - old_probs) |
|||
p_opt_a = r_theta * advantage |
|||
p_opt_b = ( |
|||
tf.clip_by_value( |
|||
r_theta, 1.0 - self.decay_epsilon, 1.0 + self.decay_epsilon |
|||
) |
|||
* advantage |
|||
) |
|||
self.policy_loss = -tf.reduce_mean( |
|||
tf.dynamic_partition(tf.minimum(p_opt_a, p_opt_b), self.policy.mask, 2)[1] |
|||
) |
|||
# For cleaner stats reporting |
|||
self.abs_policy_loss = tf.abs(self.policy_loss) |
|||
|
|||
self.loss = ( |
|||
self.policy_loss |
|||
+ 0.5 * self.value_loss |
|||
- self.decay_beta |
|||
* tf.reduce_mean(tf.dynamic_partition(entropy, self.policy.mask, 2)[1]) |
|||
) |
|||
|
|||
def _create_ppo_optimizer_ops(self): |
|||
self.tf_optimizer_op = self.create_optimizer_op(self.learning_rate) |
|||
self.grads = self.tf_optimizer_op.compute_gradients(self.loss) |
|||
self.update_batch = self.tf_optimizer_op.minimize(self.loss) |
|||
|
|||
@timed |
|||
def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]: |
|||
""" |
|||
Performs update on model. |
|||
:param mini_batch: Batch of experiences. |
|||
:param num_sequences: Number of sequences to process. |
|||
:return: Results of update. |
|||
""" |
|||
feed_dict = self._construct_feed_dict(batch, num_sequences) |
|||
stats_needed = self.stats_name_to_update_name |
|||
update_stats = {} |
|||
# Collect feed dicts for all reward signals. |
|||
for _, reward_signal in self.reward_signals.items(): |
|||
feed_dict.update( |
|||
reward_signal.prepare_update(self.policy, batch, num_sequences) |
|||
) |
|||
stats_needed.update(reward_signal.stats_name_to_update_name) |
|||
|
|||
update_vals = self._execute_model(feed_dict, self.update_dict) |
|||
for stat_name, update_name in stats_needed.items(): |
|||
update_stats[stat_name] = update_vals[update_name] |
|||
return update_stats |
|||
|
|||
def _construct_feed_dict( |
|||
self, mini_batch: AgentBuffer, num_sequences: int |
|||
) -> Dict[tf.Tensor, Any]: |
|||
# Do an optional burn-in for memories |
|||
num_burn_in = int(self.burn_in_ratio * self.policy.sequence_length) |
|||
burn_in_mask = np.ones((self.policy.sequence_length), dtype=np.float32) |
|||
burn_in_mask[range(0, num_burn_in)] = 0 |
|||
burn_in_mask = np.tile(burn_in_mask, num_sequences) |
|||
feed_dict = { |
|||
self.policy.batch_size_ph: num_sequences, |
|||
self.policy.sequence_length_ph: self.policy.sequence_length, |
|||
self.policy.mask_input: mini_batch["masks"] * burn_in_mask, |
|||
self.advantage: mini_batch["advantages"], |
|||
} |
|||
for name in self.reward_signals: |
|||
feed_dict[self.returns_holders[name]] = mini_batch[f"{name}_returns"] |
|||
feed_dict[self.old_values[name]] = mini_batch[f"{name}_value_estimates"] |
|||
|
|||
if self.policy.use_continuous_act: # For hybrid action buffer support |
|||
feed_dict[self.all_old_log_probs] = mini_batch["continuous_log_probs"] |
|||
feed_dict[self.policy.output_pre] = mini_batch["continuous_action"] |
|||
else: |
|||
feed_dict[self.all_old_log_probs] = mini_batch["discrete_log_probs"] |
|||
feed_dict[self.policy.output] = mini_batch["discrete_action"] |
|||
if self.policy.use_recurrent: |
|||
feed_dict[self.policy.prev_action] = mini_batch["prev_action"] |
|||
feed_dict[self.policy.action_masks] = mini_batch["action_mask"] |
|||
if "vector_obs" in mini_batch: |
|||
feed_dict[self.policy.vector_in] = mini_batch["vector_obs"] |
|||
if self.policy.vis_obs_size > 0: |
|||
for i, _ in enumerate(self.policy.visual_in): |
|||
feed_dict[self.policy.visual_in[i]] = mini_batch["visual_obs%d" % i] |
|||
if self.policy.use_recurrent: |
|||
feed_dict[self.policy.memory_in] = [ |
|||
mini_batch["memory"][i] |
|||
for i in range( |
|||
0, len(mini_batch["memory"]), self.policy.sequence_length |
|||
) |
|||
] |
|||
feed_dict[self.memory_in] = self._make_zero_mem( |
|||
self.m_size, mini_batch.num_experiences |
|||
) |
|||
return feed_dict |
|||
|
|||
from typing import Dict, Optional |
|||
from mlagents.tf_utils import tf |
|||
from mlagents.trainers.tf.models import ModelUtils |
|||
from mlagents.trainers.settings import EncoderType |
|||
|
|||
LOG_STD_MAX = 2 |
|||
LOG_STD_MIN = -20 |
|||
EPSILON = 1e-6 # Small value to avoid divide by zero |
|||
DISCRETE_TARGET_ENTROPY_SCALE = 0.2 # Roughly equal to e-greedy 0.05 |
|||
CONTINUOUS_TARGET_ENTROPY_SCALE = 1.0 # TODO: Make these an optional hyperparam. |
|||
POLICY_SCOPE = "" |
|||
TARGET_SCOPE = "target_network" |
|||
|
|||
|
|||
class SACNetwork: |
|||
""" |
|||
Base class for an SAC network. Implements methods for creating the actor and critic heads. |
|||
""" |
|||
|
|||
def __init__( |
|||
self, |
|||
policy=None, |
|||
m_size=None, |
|||
h_size=128, |
|||
normalize=False, |
|||
use_recurrent=False, |
|||
num_layers=2, |
|||
stream_names=None, |
|||
vis_encode_type=EncoderType.SIMPLE, |
|||
): |
|||
self.normalize = normalize |
|||
self.use_recurrent = use_recurrent |
|||
self.num_layers = num_layers |
|||
self.stream_names = stream_names |
|||
self.h_size = h_size |
|||
self.activ_fn = ModelUtils.swish |
|||
|
|||
self.sequence_length_ph = tf.placeholder( |
|||
shape=None, dtype=tf.int32, name="sac_sequence_length" |
|||
) |
|||
|
|||
self.policy_memory_in: Optional[tf.Tensor] = None |
|||
self.policy_memory_out: Optional[tf.Tensor] = None |
|||
self.value_memory_in: Optional[tf.Tensor] = None |
|||
self.value_memory_out: Optional[tf.Tensor] = None |
|||
self.q1: Optional[tf.Tensor] = None |
|||
self.q2: Optional[tf.Tensor] = None |
|||
self.q1_p: Optional[tf.Tensor] = None |
|||
self.q2_p: Optional[tf.Tensor] = None |
|||
self.q1_memory_in: Optional[tf.Tensor] = None |
|||
self.q2_memory_in: Optional[tf.Tensor] = None |
|||
self.q1_memory_out: Optional[tf.Tensor] = None |
|||
self.q2_memory_out: Optional[tf.Tensor] = None |
|||
self.prev_action: Optional[tf.Tensor] = None |
|||
self.action_masks: Optional[tf.Tensor] = None |
|||
self.external_action_in: Optional[tf.Tensor] = None |
|||
self.log_sigma_sq: Optional[tf.Tensor] = None |
|||
self.entropy: Optional[tf.Tensor] = None |
|||
self.deterministic_output: Optional[tf.Tensor] = None |
|||
self.normalized_logprobs: Optional[tf.Tensor] = None |
|||
self.action_probs: Optional[tf.Tensor] = None |
|||
self.output_oh: Optional[tf.Tensor] = None |
|||
self.output_pre: Optional[tf.Tensor] = None |
|||
|
|||
self.value_vars = None |
|||
self.q_vars = None |
|||
self.critic_vars = None |
|||
self.policy_vars = None |
|||
|
|||
self.q1_heads: Dict[str, tf.Tensor] = None |
|||
self.q2_heads: Dict[str, tf.Tensor] = None |
|||
self.q1_pheads: Dict[str, tf.Tensor] = None |
|||
self.q2_pheads: Dict[str, tf.Tensor] = None |
|||
|
|||
self.policy = policy |
|||
|
|||
def get_vars(self, scope): |
|||
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope) |
|||
|
|||
def join_scopes(self, scope_1, scope_2): |
|||
""" |
|||
Joins two scopes. Does so safetly (i.e., if one of the two scopes doesn't |
|||
exist, don't add any backslashes) |
|||
""" |
|||
if not scope_1: |
|||
return scope_2 |
|||
if not scope_2: |
|||
return scope_1 |
|||
else: |
|||
return "/".join(filter(None, [scope_1, scope_2])) |
|||
|
|||
def create_value_heads(self, stream_names, hidden_input): |
|||
""" |
|||
Creates one value estimator head for each reward signal in stream_names. |
|||
Also creates the node corresponding to the mean of all the value heads in self.value. |
|||
self.value_head is a dictionary of stream name to node containing the value estimator head for that signal. |
|||
:param stream_names: The list of reward signal names |
|||
:param hidden_input: The last layer of the Critic. The heads will consist of one dense hidden layer on top |
|||
of the hidden input. |
|||
""" |
|||
self.value_heads = {} |
|||
for name in stream_names: |
|||
value = tf.layers.dense(hidden_input, 1, name=f"{name}_value") |
|||
self.value_heads[name] = value |
|||
self.value = tf.reduce_mean(list(self.value_heads.values()), 0) |
|||
|
|||
def _create_cc_critic(self, hidden_value, scope, create_qs=True): |
|||
""" |
|||
Creates just the critic network |
|||
""" |
|||
scope = self.join_scopes(scope, "critic") |
|||
self.create_sac_value_head( |
|||
self.stream_names, |
|||
hidden_value, |
|||
self.num_layers, |
|||
self.h_size, |
|||
self.join_scopes(scope, "value"), |
|||
) |
|||
self.external_action_in = tf.placeholder( |
|||
shape=[None, self.policy.act_size[0]], |
|||
dtype=tf.float32, |
|||
name="external_action_in", |
|||
) |
|||
self.value_vars = self.get_vars(self.join_scopes(scope, "value")) |
|||
if create_qs: |
|||
hidden_q = tf.concat([hidden_value, self.external_action_in], axis=-1) |
|||
hidden_qp = tf.concat([hidden_value, self.policy.output], axis=-1) |
|||
self.q1_heads, self.q2_heads, self.q1, self.q2 = self.create_q_heads( |
|||
self.stream_names, |
|||
hidden_q, |
|||
self.num_layers, |
|||
self.h_size, |
|||
self.join_scopes(scope, "q"), |
|||
) |
|||
self.q1_pheads, self.q2_pheads, self.q1_p, self.q2_p = self.create_q_heads( |
|||
self.stream_names, |
|||
hidden_qp, |
|||
self.num_layers, |
|||
self.h_size, |
|||
self.join_scopes(scope, "q"), |
|||
reuse=True, |
|||
) |
|||
self.q_vars = self.get_vars(self.join_scopes(scope, "q")) |
|||
self.critic_vars = self.get_vars(scope) |
|||
|
|||
def _create_dc_critic(self, hidden_value, scope, create_qs=True): |
|||
""" |
|||
Creates just the critic network |
|||
""" |
|||
scope = self.join_scopes(scope, "critic") |
|||
self.create_sac_value_head( |
|||
self.stream_names, |
|||
hidden_value, |
|||
self.num_layers, |
|||
self.h_size, |
|||
self.join_scopes(scope, "value"), |
|||
) |
|||
|
|||
self.value_vars = self.get_vars("/".join([scope, "value"])) |
|||
|
|||
if create_qs: |
|||
self.q1_heads, self.q2_heads, self.q1, self.q2 = self.create_q_heads( |
|||
self.stream_names, |
|||
hidden_value, |
|||
self.num_layers, |
|||
self.h_size, |
|||
self.join_scopes(scope, "q"), |
|||
num_outputs=sum(self.policy.act_size), |
|||
) |
|||
self.q1_pheads, self.q2_pheads, self.q1_p, self.q2_p = self.create_q_heads( |
|||
self.stream_names, |
|||
hidden_value, |
|||
self.num_layers, |
|||
self.h_size, |
|||
self.join_scopes(scope, "q"), |
|||
reuse=True, |
|||
num_outputs=sum(self.policy.act_size), |
|||
) |
|||
self.q_vars = self.get_vars(scope) |
|||
self.critic_vars = self.get_vars(scope) |
|||
|
|||
def create_sac_value_head( |
|||
self, stream_names, hidden_input, num_layers, h_size, scope |
|||
): |
|||
""" |
|||
Creates one value estimator head for each reward signal in stream_names. |
|||
Also creates the node corresponding to the mean of all the value heads in self.value. |
|||
self.value_head is a dictionary of stream name to node containing the value estimator head for that signal. |
|||
:param stream_names: The list of reward signal names |
|||
:param hidden_input: The last layer of the Critic. The heads will consist of one dense hidden layer on top |
|||
of the hidden input. |
|||
:param num_layers: Number of hidden layers for value network |
|||
:param h_size: size of hidden layers for value network |
|||
:param scope: TF scope for value network. |
|||
""" |
|||
with tf.variable_scope(scope): |
|||
value_hidden = ModelUtils.create_vector_observation_encoder( |
|||
hidden_input, h_size, self.activ_fn, num_layers, "encoder", False |
|||
) |
|||
if self.use_recurrent: |
|||
value_hidden, memory_out = ModelUtils.create_recurrent_encoder( |
|||
value_hidden, |
|||
self.value_memory_in, |
|||
self.sequence_length_ph, |
|||
name="lstm_value", |
|||
) |
|||
self.value_memory_out = memory_out |
|||
self.create_value_heads(stream_names, value_hidden) |
|||
|
|||
def create_q_heads( |
|||
self, |
|||
stream_names, |
|||
hidden_input, |
|||
num_layers, |
|||
h_size, |
|||
scope, |
|||
reuse=False, |
|||
num_outputs=1, |
|||
): |
|||
""" |
|||
Creates two q heads for each reward signal in stream_names. |
|||
Also creates the node corresponding to the mean of all the value heads in self.value. |
|||
self.value_head is a dictionary of stream name to node containing the value estimator head for that signal. |
|||
:param stream_names: The list of reward signal names |
|||
:param hidden_input: The last layer of the Critic. The heads will consist of one dense hidden layer on top |
|||
of the hidden input. |
|||
:param num_layers: Number of hidden layers for Q network |
|||
:param h_size: size of hidden layers for Q network |
|||
:param scope: TF scope for Q network. |
|||
:param reuse: Whether or not to reuse variables. Useful for creating Q of policy. |
|||
:param num_outputs: Number of outputs of each Q function. If discrete, equal to number of actions. |
|||
""" |
|||
with tf.variable_scope(self.join_scopes(scope, "q1_encoding"), reuse=reuse): |
|||
q1_hidden = ModelUtils.create_vector_observation_encoder( |
|||
hidden_input, h_size, self.activ_fn, num_layers, "q1_encoder", reuse |
|||
) |
|||
if self.use_recurrent: |
|||
q1_hidden, memory_out = ModelUtils.create_recurrent_encoder( |
|||
q1_hidden, |
|||
self.q1_memory_in, |
|||
self.sequence_length_ph, |
|||
name="lstm_q1", |
|||
) |
|||
self.q1_memory_out = memory_out |
|||
|
|||
q1_heads = {} |
|||
for name in stream_names: |
|||
_q1 = tf.layers.dense(q1_hidden, num_outputs, name=f"{name}_q1") |
|||
q1_heads[name] = _q1 |
|||
|
|||
q1 = tf.reduce_mean(list(q1_heads.values()), axis=0) |
|||
with tf.variable_scope(self.join_scopes(scope, "q2_encoding"), reuse=reuse): |
|||
q2_hidden = ModelUtils.create_vector_observation_encoder( |
|||
hidden_input, h_size, self.activ_fn, num_layers, "q2_encoder", reuse |
|||
) |
|||
if self.use_recurrent: |
|||
q2_hidden, memory_out = ModelUtils.create_recurrent_encoder( |
|||
q2_hidden, |
|||
self.q2_memory_in, |
|||
self.sequence_length_ph, |
|||
name="lstm_q2", |
|||
) |
|||
self.q2_memory_out = memory_out |
|||
|
|||
q2_heads = {} |
|||
for name in stream_names: |
|||
_q2 = tf.layers.dense(q2_hidden, num_outputs, name=f"{name}_q2") |
|||
q2_heads[name] = _q2 |
|||
|
|||
q2 = tf.reduce_mean(list(q2_heads.values()), axis=0) |
|||
|
|||
return q1_heads, q2_heads, q1, q2 |
|||
|
|||
|
|||
class SACTargetNetwork(SACNetwork): |
|||
""" |
|||
Instantiation for the SAC target network. Only contains a single |
|||
value estimator and is updated from the Policy Network. |
|||
""" |
|||
|
|||
def __init__( |
|||
self, |
|||
policy, |
|||
m_size=None, |
|||
h_size=128, |
|||
normalize=False, |
|||
use_recurrent=False, |
|||
num_layers=2, |
|||
stream_names=None, |
|||
vis_encode_type=EncoderType.SIMPLE, |
|||
): |
|||
super().__init__( |
|||
policy, |
|||
m_size, |
|||
h_size, |
|||
normalize, |
|||
use_recurrent, |
|||
num_layers, |
|||
stream_names, |
|||
vis_encode_type, |
|||
) |
|||
with tf.variable_scope(TARGET_SCOPE): |
|||
self.vector_in, self.visual_in = ModelUtils.create_input_placeholders( |
|||
self.policy.behavior_spec.observation_shapes |
|||
) |
|||
if self.policy.normalize: |
|||
normalization_tensors = ModelUtils.create_normalizer(self.vector_in) |
|||
self.update_normalization_op = normalization_tensors.update_op |
|||
self.normalization_steps = normalization_tensors.steps |
|||
self.running_mean = normalization_tensors.running_mean |
|||
self.running_variance = normalization_tensors.running_variance |
|||
self.processed_vector_in = ModelUtils.normalize_vector_obs( |
|||
self.vector_in, |
|||
self.running_mean, |
|||
self.running_variance, |
|||
self.normalization_steps, |
|||
) |
|||
else: |
|||
self.processed_vector_in = self.vector_in |
|||
self.update_normalization_op = None |
|||
|
|||
if self.policy.use_recurrent: |
|||
self.memory_in = tf.placeholder( |
|||
shape=[None, m_size], dtype=tf.float32, name="target_recurrent_in" |
|||
) |
|||
self.value_memory_in = self.memory_in |
|||
hidden_streams = ModelUtils.create_observation_streams( |
|||
self.visual_in, |
|||
self.processed_vector_in, |
|||
1, |
|||
self.h_size, |
|||
0, |
|||
vis_encode_type=vis_encode_type, |
|||
stream_scopes=["critic/value/"], |
|||
) |
|||
if self.policy.use_continuous_act: |
|||
self._create_cc_critic(hidden_streams[0], TARGET_SCOPE, create_qs=False) |
|||
else: |
|||
self._create_dc_critic(hidden_streams[0], TARGET_SCOPE, create_qs=False) |
|||
if self.use_recurrent: |
|||
self.memory_out = tf.concat( |
|||
self.value_memory_out, axis=1 |
|||
) # Needed for Barracuda to work |
|||
|
|||
def copy_normalization(self, mean, variance, steps): |
|||
""" |
|||
Copies the mean, variance, and steps into the normalizers of the |
|||
input of this SACNetwork. Used to copy the normalizer from the policy network |
|||
to the target network. |
|||
param mean: Tensor containing the mean. |
|||
param variance: Tensor containing the variance |
|||
param steps: Tensor containing the number of steps. |
|||
""" |
|||
update_mean = tf.assign(self.running_mean, mean) |
|||
update_variance = tf.assign(self.running_variance, variance) |
|||
update_norm_step = tf.assign(self.normalization_steps, steps) |
|||
return tf.group([update_mean, update_variance, update_norm_step]) |
|||
|
|||
|
|||
class SACPolicyNetwork(SACNetwork): |
|||
""" |
|||
Instantiation for SAC policy network. Contains a dual Q estimator, |
|||
a value estimator, and a reference to the actual policy network. |
|||
""" |
|||
|
|||
def __init__( |
|||
self, |
|||
policy, |
|||
m_size=None, |
|||
h_size=128, |
|||
normalize=False, |
|||
use_recurrent=False, |
|||
num_layers=2, |
|||
stream_names=None, |
|||
vis_encode_type=EncoderType.SIMPLE, |
|||
): |
|||
super().__init__( |
|||
policy, |
|||
m_size, |
|||
h_size, |
|||
normalize, |
|||
use_recurrent, |
|||
num_layers, |
|||
stream_names, |
|||
vis_encode_type, |
|||
) |
|||
if self.policy.use_recurrent: |
|||
self._create_memory_ins(m_size) |
|||
|
|||
hidden_critic = self._create_observation_in(vis_encode_type) |
|||
# Use the sequence length of the policy |
|||
self.sequence_length_ph = self.policy.sequence_length_ph |
|||
|
|||
if self.policy.use_continuous_act: |
|||
self._create_cc_critic(hidden_critic, POLICY_SCOPE) |
|||
|
|||
else: |
|||
self._create_dc_critic(hidden_critic, POLICY_SCOPE) |
|||
|
|||
if self.use_recurrent: |
|||
mem_outs = [self.value_memory_out, self.q1_memory_out, self.q2_memory_out] |
|||
self.memory_out = tf.concat(mem_outs, axis=1) |
|||
|
|||
def _create_memory_ins(self, m_size): |
|||
""" |
|||
Creates the memory input placeholders for LSTM. |
|||
:param m_size: the total size of the memory. |
|||
""" |
|||
self.memory_in = tf.placeholder( |
|||
shape=[None, m_size * 3], dtype=tf.float32, name="value_recurrent_in" |
|||
) |
|||
|
|||
# Re-break-up for each network |
|||
num_mems = 3 |
|||
input_size = self.memory_in.get_shape().as_list()[1] |
|||
mem_ins = [] |
|||
for i in range(num_mems): |
|||
_start = input_size // num_mems * i |
|||
_end = input_size // num_mems * (i + 1) |
|||
mem_ins.append(self.memory_in[:, _start:_end]) |
|||
self.value_memory_in = mem_ins[0] |
|||
self.q1_memory_in = mem_ins[1] |
|||
self.q2_memory_in = mem_ins[2] |
|||
|
|||
def _create_observation_in(self, vis_encode_type): |
|||
""" |
|||
Creates the observation inputs, and a CNN if needed, |
|||
:param vis_encode_type: Type of CNN encoder. |
|||
:param share_ac_cnn: Whether or not to share the actor and critic CNNs. |
|||
:return A tuple of (hidden_policy, hidden_critic). We don't save it to self since they're used |
|||
once and thrown away. |
|||
""" |
|||
with tf.variable_scope(POLICY_SCOPE): |
|||
hidden_streams = ModelUtils.create_observation_streams( |
|||
self.policy.visual_in, |
|||
self.policy.processed_vector_in, |
|||
1, |
|||
self.h_size, |
|||
0, |
|||
vis_encode_type=vis_encode_type, |
|||
stream_scopes=["critic/value/"], |
|||
) |
|||
hidden_critic = hidden_streams[0] |
|||
return hidden_critic |
|||
|
|||
import numpy as np |
|||
from typing import Dict, List, Optional, Any, Mapping, cast |
|||
|
|||
from mlagents.tf_utils import tf |
|||
|
|||
from mlagents_envs.logging_util import get_logger |
|||
from mlagents.trainers.sac.network import SACPolicyNetwork, SACTargetNetwork |
|||
from mlagents.trainers.tf.models import ModelUtils |
|||
from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer |
|||
from mlagents.trainers.policy.tf_policy import TFPolicy |
|||
from mlagents.trainers.buffer import AgentBuffer |
|||
from mlagents_envs.timers import timed |
|||
from mlagents.trainers.settings import TrainerSettings, SACSettings |
|||
|
|||
EPSILON = 1e-6 # Small value to avoid divide by zero |
|||
|
|||
logger = get_logger(__name__) |
|||
|
|||
POLICY_SCOPE = "" |
|||
TARGET_SCOPE = "target_network" |
|||
|
|||
|
|||
class SACOptimizer(TFOptimizer): |
|||
def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings): |
|||
""" |
|||
Takes a Unity environment and model-specific hyper-parameters and returns the |
|||
appropriate PPO agent model for the environment. |
|||
:param brain: Brain parameters used to generate specific network graph. |
|||
:param lr: Learning rate. |
|||
:param lr_schedule: Learning rate decay schedule. |
|||
:param h_size: Size of hidden layers |
|||
:param init_entcoef: Initial value for entropy coefficient. Set lower to learn faster, |
|||
set higher to explore more. |
|||
:return: a sub-class of PPOAgent tailored to the environment. |
|||
:param max_step: Total number of training steps. |
|||
:param normalize: Whether to normalize vector observation input. |
|||
:param use_recurrent: Whether to use an LSTM layer in the network. |
|||
:param num_layers: Number of hidden layers between encoded input and policy & value layers |
|||
:param tau: Strength of soft-Q update. |
|||
:param m_size: Size of brain memory. |
|||
""" |
|||
# Create the graph here to give more granular control of the TF graph to the Optimizer. |
|||
policy.create_tf_graph() |
|||
|
|||
with policy.graph.as_default(): |
|||
with tf.variable_scope(""): |
|||
super().__init__(policy, trainer_params) |
|||
hyperparameters: SACSettings = cast( |
|||
SACSettings, trainer_params.hyperparameters |
|||
) |
|||
lr = hyperparameters.learning_rate |
|||
lr_schedule = hyperparameters.learning_rate_schedule |
|||
max_step = trainer_params.max_steps |
|||
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 |
|||
h_size = policy_network_settings.hidden_units |
|||
num_layers = policy_network_settings.num_layers |
|||
vis_encode_type = policy_network_settings.vis_encode_type |
|||
|
|||
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 |
|||
|
|||
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: tf.Variable(1.0) for name in stream_names |
|||
} |
|||
self.disable_use_dones = { |
|||
name: self.use_dones_in_backup[name].assign(0.0) |
|||
for name in stream_names |
|||
} |
|||
|
|||
if num_layers < 1: |
|||
num_layers = 1 |
|||
|
|||
self.target_init_op: List[tf.Tensor] = [] |
|||
self.target_update_op: List[tf.Tensor] = [] |
|||
self.update_batch_policy: Optional[tf.Operation] = None |
|||
self.update_batch_value: Optional[tf.Operation] = None |
|||
self.update_batch_entropy: Optional[tf.Operation] = None |
|||
|
|||
self.policy_network = SACPolicyNetwork( |
|||
policy=self.policy, |
|||
m_size=self.policy.m_size, # 3x policy.m_size |
|||
h_size=h_size, |
|||
normalize=self.policy.normalize, |
|||
use_recurrent=self.policy.use_recurrent, |
|||
num_layers=num_layers, |
|||
stream_names=stream_names, |
|||
vis_encode_type=vis_encode_type, |
|||
) |
|||
self.target_network = SACTargetNetwork( |
|||
policy=self.policy, |
|||
m_size=self.policy.m_size, # 1x policy.m_size |
|||
h_size=h_size, |
|||
normalize=self.policy.normalize, |
|||
use_recurrent=self.policy.use_recurrent, |
|||
num_layers=num_layers, |
|||
stream_names=stream_names, |
|||
vis_encode_type=vis_encode_type, |
|||
) |
|||
# The optimizer's m_size is 3 times the policy (Q1, Q2, and Value) |
|||
self.m_size = 3 * self.policy.m_size |
|||
self._create_inputs_and_outputs() |
|||
self.learning_rate = ModelUtils.create_schedule( |
|||
lr_schedule, |
|||
lr, |
|||
self.policy.global_step, |
|||
int(max_step), |
|||
min_value=1e-10, |
|||
) |
|||
self._create_losses( |
|||
self.policy_network.q1_heads, |
|||
self.policy_network.q2_heads, |
|||
lr, |
|||
int(max_step), |
|||
stream_names, |
|||
discrete=not self.policy.use_continuous_act, |
|||
) |
|||
self._create_sac_optimizer_ops() |
|||
|
|||
self.selected_actions = ( |
|||
self.policy.selected_actions |
|||
) # For GAIL and other reward signals |
|||
if self.policy.normalize: |
|||
target_update_norm = self.target_network.copy_normalization( |
|||
self.policy.running_mean, |
|||
self.policy.running_variance, |
|||
self.policy.normalization_steps, |
|||
) |
|||
# Update the normalization of the optimizer when the policy does. |
|||
self.policy.update_normalization_op = tf.group( |
|||
[self.policy.update_normalization_op, target_update_norm] |
|||
) |
|||
|
|||
self.stats_name_to_update_name = { |
|||
"Losses/Value Loss": "value_loss", |
|||
"Losses/Policy Loss": "policy_loss", |
|||
"Losses/Q1 Loss": "q1_loss", |
|||
"Losses/Q2 Loss": "q2_loss", |
|||
"Policy/Entropy Coeff": "entropy_coef", |
|||
"Policy/Learning Rate": "learning_rate", |
|||
} |
|||
|
|||
self.update_dict = { |
|||
"value_loss": self.total_value_loss, |
|||
"policy_loss": self.policy_loss, |
|||
"q1_loss": self.q1_loss, |
|||
"q2_loss": self.q2_loss, |
|||
"entropy_coef": self.ent_coef, |
|||
"update_batch": self.update_batch_policy, |
|||
"update_value": self.update_batch_value, |
|||
"update_entropy": self.update_batch_entropy, |
|||
"learning_rate": self.learning_rate, |
|||
} |
|||
|
|||
def _create_inputs_and_outputs(self) -> None: |
|||
""" |
|||
Assign the higher-level SACModel's inputs and outputs to those of its policy or |
|||
target network. |
|||
""" |
|||
self.vector_in = self.policy.vector_in |
|||
self.visual_in = self.policy.visual_in |
|||
self.next_vector_in = self.target_network.vector_in |
|||
self.next_visual_in = self.target_network.visual_in |
|||
self.sequence_length_ph = self.policy.sequence_length_ph |
|||
self.next_sequence_length_ph = self.target_network.sequence_length_ph |
|||
if not self.policy.use_continuous_act: |
|||
self.action_masks = self.policy_network.action_masks |
|||
else: |
|||
self.output_pre = self.policy_network.output_pre |
|||
|
|||
# Don't use value estimate during inference. |
|||
self.value = tf.identity( |
|||
self.policy_network.value, name="value_estimate_unused" |
|||
) |
|||
self.value_heads = self.policy_network.value_heads |
|||
self.dones_holder = tf.placeholder( |
|||
shape=[None], dtype=tf.float32, name="dones_holder" |
|||
) |
|||
|
|||
if self.policy.use_recurrent: |
|||
self.memory_in = self.policy_network.memory_in |
|||
self.memory_out = self.policy_network.memory_out |
|||
if not self.policy.use_continuous_act: |
|||
self.prev_action = self.policy_network.prev_action |
|||
self.next_memory_in = self.target_network.memory_in |
|||
|
|||
def _create_losses( |
|||
self, |
|||
q1_streams: Dict[str, tf.Tensor], |
|||
q2_streams: Dict[str, tf.Tensor], |
|||
lr: tf.Tensor, |
|||
max_step: int, |
|||
stream_names: List[str], |
|||
discrete: bool = False, |
|||
) -> None: |
|||
""" |
|||
Creates training-specific Tensorflow ops for SAC models. |
|||
:param q1_streams: Q1 streams from policy network |
|||
:param q1_streams: Q2 streams from policy network |
|||
:param lr: Learning rate |
|||
:param max_step: Total number of training steps. |
|||
:param stream_names: List of reward stream names. |
|||
:param discrete: Whether or not to use discrete action losses. |
|||
""" |
|||
|
|||
if discrete: |
|||
self.target_entropy = [ |
|||
self.discrete_target_entropy_scale * np.log(i).astype(np.float32) |
|||
for i in self.act_size |
|||
] |
|||
discrete_action_probs = tf.exp(self.policy.all_log_probs) |
|||
per_action_entropy = discrete_action_probs * self.policy.all_log_probs |
|||
else: |
|||
self.target_entropy = ( |
|||
-1 |
|||
* self.continuous_target_entropy_scale |
|||
* np.prod(self.act_size[0]).astype(np.float32) |
|||
) |
|||
|
|||
self.rewards_holders = {} |
|||
self.min_policy_qs = {} |
|||
|
|||
for name in stream_names: |
|||
if discrete: |
|||
_branched_mpq1 = ModelUtils.break_into_branches( |
|||
self.policy_network.q1_pheads[name] * discrete_action_probs, |
|||
self.act_size, |
|||
) |
|||
branched_mpq1 = tf.stack( |
|||
[ |
|||
tf.reduce_sum(_br, axis=1, keep_dims=True) |
|||
for _br in _branched_mpq1 |
|||
] |
|||
) |
|||
_q1_p_mean = tf.reduce_mean(branched_mpq1, axis=0) |
|||
|
|||
_branched_mpq2 = ModelUtils.break_into_branches( |
|||
self.policy_network.q2_pheads[name] * discrete_action_probs, |
|||
self.act_size, |
|||
) |
|||
branched_mpq2 = tf.stack( |
|||
[ |
|||
tf.reduce_sum(_br, axis=1, keep_dims=True) |
|||
for _br in _branched_mpq2 |
|||
] |
|||
) |
|||
_q2_p_mean = tf.reduce_mean(branched_mpq2, axis=0) |
|||
|
|||
self.min_policy_qs[name] = tf.minimum(_q1_p_mean, _q2_p_mean) |
|||
else: |
|||
self.min_policy_qs[name] = tf.minimum( |
|||
self.policy_network.q1_pheads[name], |
|||
self.policy_network.q2_pheads[name], |
|||
) |
|||
|
|||
rewards_holder = tf.placeholder( |
|||
shape=[None], dtype=tf.float32, name=f"{name}_rewards" |
|||
) |
|||
self.rewards_holders[name] = rewards_holder |
|||
|
|||
q1_losses = [] |
|||
q2_losses = [] |
|||
# Multiple q losses per stream |
|||
expanded_dones = tf.expand_dims(self.dones_holder, axis=-1) |
|||
for i, name in enumerate(stream_names): |
|||
_expanded_rewards = tf.expand_dims(self.rewards_holders[name], axis=-1) |
|||
|
|||
q_backup = tf.stop_gradient( |
|||
_expanded_rewards |
|||
+ (1.0 - self.use_dones_in_backup[name] * expanded_dones) |
|||
* self.gammas[i] |
|||
* self.target_network.value_heads[name] |
|||
) |
|||
|
|||
if discrete: |
|||
# We need to break up the Q functions by branch, and update them individually. |
|||
branched_q1_stream = ModelUtils.break_into_branches( |
|||
self.policy.selected_actions * q1_streams[name], self.act_size |
|||
) |
|||
branched_q2_stream = ModelUtils.break_into_branches( |
|||
self.policy.selected_actions * q2_streams[name], self.act_size |
|||
) |
|||
|
|||
# Reduce each branch into scalar |
|||
branched_q1_stream = [ |
|||
tf.reduce_sum(_branch, axis=1, keep_dims=True) |
|||
for _branch in branched_q1_stream |
|||
] |
|||
branched_q2_stream = [ |
|||
tf.reduce_sum(_branch, axis=1, keep_dims=True) |
|||
for _branch in branched_q2_stream |
|||
] |
|||
|
|||
q1_stream = tf.reduce_mean(branched_q1_stream, axis=0) |
|||
q2_stream = tf.reduce_mean(branched_q2_stream, axis=0) |
|||
|
|||
else: |
|||
q1_stream = q1_streams[name] |
|||
q2_stream = q2_streams[name] |
|||
|
|||
_q1_loss = 0.5 * tf.reduce_mean( |
|||
tf.to_float(self.policy.mask) |
|||
* tf.squared_difference(q_backup, q1_stream) |
|||
) |
|||
|
|||
_q2_loss = 0.5 * tf.reduce_mean( |
|||
tf.to_float(self.policy.mask) |
|||
* tf.squared_difference(q_backup, q2_stream) |
|||
) |
|||
|
|||
q1_losses.append(_q1_loss) |
|||
q2_losses.append(_q2_loss) |
|||
|
|||
self.q1_loss = tf.reduce_mean(q1_losses) |
|||
self.q2_loss = tf.reduce_mean(q2_losses) |
|||
|
|||
# Learn entropy coefficient |
|||
if discrete: |
|||
# Create a log_ent_coef for each branch |
|||
self.log_ent_coef = tf.get_variable( |
|||
"log_ent_coef", |
|||
dtype=tf.float32, |
|||
initializer=np.log([self.init_entcoef] * len(self.act_size)).astype( |
|||
np.float32 |
|||
), |
|||
trainable=True, |
|||
) |
|||
else: |
|||
self.log_ent_coef = tf.get_variable( |
|||
"log_ent_coef", |
|||
dtype=tf.float32, |
|||
initializer=np.log(self.init_entcoef).astype(np.float32), |
|||
trainable=True, |
|||
) |
|||
|
|||
self.ent_coef = tf.exp(self.log_ent_coef) |
|||
if discrete: |
|||
# We also have to do a different entropy and target_entropy per branch. |
|||
branched_per_action_ent = ModelUtils.break_into_branches( |
|||
per_action_entropy, self.act_size |
|||
) |
|||
branched_ent_sums = tf.stack( |
|||
[ |
|||
tf.reduce_sum(_lp, axis=1, keep_dims=True) + _te |
|||
for _lp, _te in zip(branched_per_action_ent, self.target_entropy) |
|||
], |
|||
axis=1, |
|||
) |
|||
self.entropy_loss = -tf.reduce_mean( |
|||
tf.to_float(self.policy.mask) |
|||
* tf.reduce_mean( |
|||
self.log_ent_coef |
|||
* tf.squeeze(tf.stop_gradient(branched_ent_sums), axis=2), |
|||
axis=1, |
|||
) |
|||
) |
|||
|
|||
# Same with policy loss, we have to do the loss per branch and average them, |
|||
# so that larger branches don't get more weight. |
|||
# The equivalent KL divergence from Eq 10 of Haarnoja et al. is also pi*log(pi) - Q |
|||
branched_q_term = ModelUtils.break_into_branches( |
|||
discrete_action_probs * self.policy_network.q1_p, self.act_size |
|||
) |
|||
|
|||
branched_policy_loss = tf.stack( |
|||
[ |
|||
tf.reduce_sum(self.ent_coef[i] * _lp - _qt, axis=1, keep_dims=True) |
|||
for i, (_lp, _qt) in enumerate( |
|||
zip(branched_per_action_ent, branched_q_term) |
|||
) |
|||
] |
|||
) |
|||
self.policy_loss = tf.reduce_mean( |
|||
tf.to_float(self.policy.mask) * tf.squeeze(branched_policy_loss) |
|||
) |
|||
|
|||
# Do vbackup entropy bonus per branch as well. |
|||
branched_ent_bonus = tf.stack( |
|||
[ |
|||
tf.reduce_sum(self.ent_coef[i] * _lp, axis=1, keep_dims=True) |
|||
for i, _lp in enumerate(branched_per_action_ent) |
|||
] |
|||
) |
|||
value_losses = [] |
|||
for name in stream_names: |
|||
v_backup = tf.stop_gradient( |
|||
self.min_policy_qs[name] |
|||
- tf.reduce_mean(branched_ent_bonus, axis=0) |
|||
) |
|||
value_losses.append( |
|||
0.5 |
|||
* tf.reduce_mean( |
|||
tf.to_float(self.policy.mask) |
|||
* tf.squared_difference( |
|||
self.policy_network.value_heads[name], v_backup |
|||
) |
|||
) |
|||
) |
|||
|
|||
else: |
|||
self.entropy_loss = -tf.reduce_mean( |
|||
self.log_ent_coef |
|||
* tf.to_float(self.policy.mask) |
|||
* tf.stop_gradient( |
|||
tf.reduce_sum( |
|||
self.policy.all_log_probs + self.target_entropy, |
|||
axis=1, |
|||
keep_dims=True, |
|||
) |
|||
) |
|||
) |
|||
batch_policy_loss = tf.reduce_mean( |
|||
self.ent_coef * self.policy.all_log_probs - self.policy_network.q1_p, |
|||
axis=1, |
|||
) |
|||
self.policy_loss = tf.reduce_mean( |
|||
tf.to_float(self.policy.mask) * batch_policy_loss |
|||
) |
|||
|
|||
value_losses = [] |
|||
for name in stream_names: |
|||
v_backup = tf.stop_gradient( |
|||
self.min_policy_qs[name] |
|||
- tf.reduce_sum(self.ent_coef * self.policy.all_log_probs, axis=1) |
|||
) |
|||
value_losses.append( |
|||
0.5 |
|||
* tf.reduce_mean( |
|||
tf.to_float(self.policy.mask) |
|||
* tf.squared_difference( |
|||
self.policy_network.value_heads[name], v_backup |
|||
) |
|||
) |
|||
) |
|||
self.value_loss = tf.reduce_mean(value_losses) |
|||
|
|||
self.total_value_loss = self.q1_loss + self.q2_loss + self.value_loss |
|||
|
|||
self.entropy = self.policy_network.entropy |
|||
|
|||
def _create_sac_optimizer_ops(self) -> None: |
|||
""" |
|||
Creates the Adam optimizers and update ops for SAC, including |
|||
the policy, value, and entropy updates, as well as the target network update. |
|||
""" |
|||
policy_optimizer = self.create_optimizer_op( |
|||
learning_rate=self.learning_rate, name="sac_policy_opt" |
|||
) |
|||
entropy_optimizer = self.create_optimizer_op( |
|||
learning_rate=self.learning_rate, name="sac_entropy_opt" |
|||
) |
|||
value_optimizer = self.create_optimizer_op( |
|||
learning_rate=self.learning_rate, name="sac_value_opt" |
|||
) |
|||
|
|||
self.target_update_op = [ |
|||
tf.assign(target, (1 - self.tau) * target + self.tau * source) |
|||
for target, source in zip( |
|||
self.target_network.value_vars, self.policy_network.value_vars |
|||
) |
|||
] |
|||
logger.debug("value_vars") |
|||
self.print_all_vars(self.policy_network.value_vars) |
|||
logger.debug("targvalue_vars") |
|||
self.print_all_vars(self.target_network.value_vars) |
|||
logger.debug("critic_vars") |
|||
self.print_all_vars(self.policy_network.critic_vars) |
|||
logger.debug("q_vars") |
|||
self.print_all_vars(self.policy_network.q_vars) |
|||
logger.debug("policy_vars") |
|||
policy_vars = self.policy.get_trainable_variables() |
|||
self.print_all_vars(policy_vars) |
|||
|
|||
self.target_init_op = [ |
|||
tf.assign(target, source) |
|||
for target, source in zip( |
|||
self.target_network.value_vars, self.policy_network.value_vars |
|||
) |
|||
] |
|||
|
|||
self.update_batch_policy = policy_optimizer.minimize( |
|||
self.policy_loss, var_list=policy_vars |
|||
) |
|||
|
|||
# Make sure policy is updated first, then value, then entropy. |
|||
with tf.control_dependencies([self.update_batch_policy]): |
|||
self.update_batch_value = value_optimizer.minimize( |
|||
self.total_value_loss, var_list=self.policy_network.critic_vars |
|||
) |
|||
# Add entropy coefficient optimization operation |
|||
with tf.control_dependencies([self.update_batch_value]): |
|||
self.update_batch_entropy = entropy_optimizer.minimize( |
|||
self.entropy_loss, var_list=self.log_ent_coef |
|||
) |
|||
|
|||
def print_all_vars(self, variables): |
|||
for _var in variables: |
|||
logger.debug(_var) |
|||
|
|||
@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. |
|||
""" |
|||
feed_dict = self._construct_feed_dict(self.policy, batch, num_sequences) |
|||
stats_needed = self.stats_name_to_update_name |
|||
update_stats: Dict[str, float] = {} |
|||
update_vals = self._execute_model(feed_dict, self.update_dict) |
|||
for stat_name, update_name in stats_needed.items(): |
|||
update_stats[stat_name] = update_vals[update_name] |
|||
# Update target network. By default, target update happens at every policy update. |
|||
self.sess.run(self.target_update_op) |
|||
return update_stats |
|||
|
|||
def update_reward_signals( |
|||
self, reward_signal_minibatches: Mapping[str, AgentBuffer], num_sequences: int |
|||
) -> Dict[str, float]: |
|||
""" |
|||
Only update the reward signals. |
|||
:param reward_signal_batches: Minibatches to use for updating the reward signals, |
|||
indexed by name. If none, don't update the reward signals. |
|||
""" |
|||
# Collect feed dicts for all reward signals. |
|||
feed_dict: Dict[tf.Tensor, Any] = {} |
|||
update_dict: Dict[str, tf.Tensor] = {} |
|||
update_stats: Dict[str, float] = {} |
|||
stats_needed: Dict[str, str] = {} |
|||
if reward_signal_minibatches: |
|||
self.add_reward_signal_dicts( |
|||
feed_dict, |
|||
update_dict, |
|||
stats_needed, |
|||
reward_signal_minibatches, |
|||
num_sequences, |
|||
) |
|||
update_vals = self._execute_model(feed_dict, update_dict) |
|||
for stat_name, update_name in stats_needed.items(): |
|||
update_stats[stat_name] = update_vals[update_name] |
|||
return update_stats |
|||
|
|||
def add_reward_signal_dicts( |
|||
self, |
|||
feed_dict: Dict[tf.Tensor, Any], |
|||
update_dict: Dict[str, tf.Tensor], |
|||
stats_needed: Dict[str, str], |
|||
reward_signal_minibatches: Mapping[str, AgentBuffer], |
|||
num_sequences: int, |
|||
) -> None: |
|||
""" |
|||
Adds the items needed for reward signal updates to the feed_dict and stats_needed dict. |
|||
:param feed_dict: Feed dict needed update |
|||
:param update_dit: Update dict that needs update |
|||
:param stats_needed: Stats needed to get from the update. |
|||
:param reward_signal_minibatches: Minibatches to use for updating the reward signals, |
|||
indexed by name. |
|||
""" |
|||
for name, r_batch in reward_signal_minibatches.items(): |
|||
feed_dict.update( |
|||
self.reward_signals[name].prepare_update( |
|||
self.policy, r_batch, num_sequences |
|||
) |
|||
) |
|||
update_dict.update(self.reward_signals[name].update_dict) |
|||
stats_needed.update(self.reward_signals[name].stats_name_to_update_name) |
|||
|
|||
def _construct_feed_dict( |
|||
self, policy: TFPolicy, batch: AgentBuffer, num_sequences: int |
|||
) -> Dict[tf.Tensor, Any]: |
|||
""" |
|||
Builds the feed dict for updating the SAC model. |
|||
:param model: The model to update. May be different when, e.g. using multi-GPU. |
|||
:param batch: Mini-batch to use to update. |
|||
:param num_sequences: Number of LSTM sequences in batch. |
|||
""" |
|||
# Do an optional burn-in for memories |
|||
num_burn_in = int(self.burn_in_ratio * self.policy.sequence_length) |
|||
burn_in_mask = np.ones((self.policy.sequence_length), dtype=np.float32) |
|||
burn_in_mask[range(0, num_burn_in)] = 0 |
|||
burn_in_mask = np.tile(burn_in_mask, num_sequences) |
|||
feed_dict = { |
|||
policy.batch_size_ph: num_sequences, |
|||
policy.sequence_length_ph: self.policy.sequence_length, |
|||
self.next_sequence_length_ph: self.policy.sequence_length, |
|||
self.policy.mask_input: batch["masks"] * burn_in_mask, |
|||
} |
|||
for name in self.reward_signals: |
|||
feed_dict[self.rewards_holders[name]] = batch[f"{name}_rewards"] |
|||
|
|||
if self.policy.use_continuous_act: |
|||
feed_dict[self.policy_network.external_action_in] = batch[ |
|||
"continuous_action" |
|||
] |
|||
else: |
|||
feed_dict[policy.output] = batch["discrete_action"] |
|||
if self.policy.use_recurrent: |
|||
feed_dict[policy.prev_action] = batch["prev_action"] |
|||
feed_dict[policy.action_masks] = batch["action_mask"] |
|||
if self.policy.use_vec_obs: |
|||
feed_dict[policy.vector_in] = batch["vector_obs"] |
|||
feed_dict[self.next_vector_in] = batch["next_vector_in"] |
|||
if self.policy.vis_obs_size > 0: |
|||
for i, _ in enumerate(policy.visual_in): |
|||
_obs = batch["visual_obs%d" % i] |
|||
feed_dict[policy.visual_in[i]] = _obs |
|||
for i, _ in enumerate(self.next_visual_in): |
|||
_obs = batch["next_visual_obs%d" % i] |
|||
feed_dict[self.next_visual_in[i]] = _obs |
|||
if self.policy.use_recurrent: |
|||
feed_dict[policy.memory_in] = [ |
|||
batch["memory"][i] |
|||
for i in range(0, len(batch["memory"]), self.policy.sequence_length) |
|||
] |
|||
feed_dict[self.policy_network.memory_in] = self._make_zero_mem( |
|||
self.m_size, batch.num_experiences |
|||
) |
|||
feed_dict[self.target_network.memory_in] = self._make_zero_mem( |
|||
self.m_size // 3, batch.num_experiences |
|||
) |
|||
feed_dict[self.dones_holder] = batch["done"] |
|||
return feed_dict |
|||
|
|||
# pip constraints to use the *lowest* versions allowed in ml-agents/setup.py |
|||
grpcio==1.11.0 |
|||
numpy==1.14.1 |
|||
Pillow==4.2.1 |
|||
protobuf==3.6 |
|||
tensorflow==1.14.0 |
|||
h5py==2.9.0 |
|||
tensorboard==1.15.0 |
|||
|
|||
# pip constraints to use the *highest* versions allowed in ml-agents/setup.py |
|||
# For projects with upper bounds, we should periodically update this list to the latest release version |
|||
grpcio>=1.23.0 |
|||
numpy>=1.17.2 |
|||
tensorflow==2.3.0 |
|||
h5py>=2.10.0 |
|||
|
|||
# pip constraints to use the *highest* versions allowed in ml-agents/setup.py |
|||
# with the exception of tensorflow, which is constrained to <2 |
|||
# For projects with upper bounds, we should periodically update this list to the latest release version |
|||
grpcio>=1.23.0 |
|||
numpy>=1.17.2 |
|||
tensorflow>=1.15.2,<2.0.0 |
|||
h5py>=2.10.0 |
|||
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