reccurent gail tests

ml-agents-envs/mlagents_envs/rpc_utils.py

 from mlagents_envs.exception import UnityObservationException
 from mlagents_envs.timers import hierarchical_timer, timed
 from mlagents_envs.communicator_objects.agent_info_pb2 import AgentInfoProto
+from mlagents_envs.communicator_objects.agent_action_pb2 import AgentActionProto
+from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
+    AgentInfoActionPairProto,
+)
-    NONE as COMPRESSION_NONE,
+    NONE as COMPRESSION_TYPE_NONE,
 )
 from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
 import numpy as np
                 f"Observation did not have the expected shape - got {obs.shape} but expected {expected_shape}"
             )
     gray_scale = obs.shape[2] == 1
-    if obs.compression_type == COMPRESSION_NONE:
+    if obs.compression_type == COMPRESSION_TYPE_NONE:
         img = np.array(obs.float_data.data, dtype=np.float32)
         img = np.reshape(img, obs.shape)
         return img
             indices = _generate_split_indices(group_spec.discrete_action_branches)
             action_mask = np.split(action_mask, indices, axis=1)
     return BatchedStepResult(obs_list, rewards, done, max_step, agent_id, action_mask)
+
+
+def proto_from_batched_step_result(
+    batched_step_result: BatchedStepResult
+) -> List[AgentInfoProto]:
+    agent_info_protos: List[AgentInfoProto] = []
+    for agent_id in batched_step_result.agent_id:
+        agent_id_index = batched_step_result.get_index(agent_id)
+        reward = batched_step_result.reward[agent_id_index]
+        done = batched_step_result.done[agent_id_index]
+        max_step_reached = batched_step_result.max_step[agent_id_index]
+        agent_mask = None
+        if batched_step_result.action_mask is not None:
+            mask = batched_step_result.action_mask[0]
+            agent_mask = mask[agent_id_index]
+        observations: List[ObservationProto] = []
+        for all_observations_of_type in batched_step_result.obs:
+            observation = all_observations_of_type[agent_id_index]
+            if len(observation.shape) == 3:
+                observations.append(
+                    ObservationProto(
+                        compressed_data=observation,
+                        shape=observation.shape,
+                        compression_type=COMPRESSION_TYPE_NONE,
+                    )
+                )
+            else:
+                observations.append(
+                    ObservationProto(
+                        float_data=ObservationProto.FloatData(data=observation),
+                        shape=[len(observation)],
+                        compression_type=COMPRESSION_TYPE_NONE,
+                    )
+                )
+
+        agent_info_proto = AgentInfoProto(
+            reward=reward,
+            done=done,
+            id=agent_id,
+            max_step_reached=max_step_reached,
+            action_mask=agent_mask,
+            observations=observations,
+        )
+        agent_info_protos.append(agent_info_proto)
+    return agent_info_protos
+
+
+# The arguments here are the BatchedStepResult and actions for a single agent name
+def proto_from_batched_step_result_and_action(
+    batched_step_result: BatchedStepResult, actions: np.ndarray
+) -> List[AgentInfoActionPairProto]:
+    agent_info_protos = proto_from_batched_step_result(batched_step_result)
+    agent_action_protos = [
+        AgentActionProto(vector_actions=action) for action in actions
+    ]
+    agent_info_action_pair_protos = [
+        AgentInfoActionPairProto(agent_info=agent_info_proto, action_info=action_proto)
+        for agent_info_proto, action_proto in zip(
+            agent_info_protos, agent_action_protos
+        )
+    ]
+    return agent_info_action_pair_protos
 
 
 def _generate_split_indices(dims):

ml-agents-envs/mlagents_envs/base_env.py

     def contains_agent(self, agent_id: AgentId) -> bool:
         return agent_id in self.agent_id_to_index
 
+    def get_index(self, agent_id: AgentId) -> int:
+        return self.agent_id_to_index.get(agent_id, -1)  # type: ignore
+
     def get_agent_step_result(self, agent_id: AgentId) -> StepResult:
         """
         returns the step result for a specific agent.
         """
         if not self.contains_agent(agent_id):
             raise IndexError(
-                "agent_id {} is not present in the BatchedStepResult".format(agent_id)
+                "get_agent_step_result failed. agent_id {} is not present in the BatchedStepResult".format(
+                    agent_id
+                )
             )
         agent_index = self._agent_id_to_index[agent_id]  # type: ignore
         agent_obs = []

ml-agents/mlagents/trainers/demo_loader.py

 )
 from mlagents_envs.timers import timed, hierarchical_timer
 from google.protobuf.internal.decoder import _DecodeVarint32  # type: ignore
+from google.protobuf.internal.encoder import _EncodeVarint  # type: ignore
 
 
 @timed
         )
 
 
+INITIAL_POS = 33
+
+
 @timed
 def load_demonstration(
     file_path: str
     """
 
     # First 32 bytes of file dedicated to meta-data.
-    INITIAL_POS = 33
     file_paths = get_demo_files(file_path)
     group_spec = None
     brain_param_proto = None
             f"No BrainParameters found in demonstration file at {file_path}."
         )
     return group_spec, info_action_pairs, total_expected
+
+
+def write_delimited(f, message):
+    msg_string = message.SerializeToString()
+    msg_size = len(msg_string)
+    _EncodeVarint(f.write, msg_size)
+    f.write(msg_string)
+
+
+def write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos):
+    with open(demo_path, "wb") as f:
+        # write metadata
+        write_delimited(f, meta_data_proto)
+        f.seek(INITIAL_POS)
+        write_delimited(f, brain_param_proto)
+
+        for agent in agent_info_protos:
+            write_delimited(f, agent)

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

     BatchedStepResult,
     ActionType,
 )
+from mlagents_envs.rpc_utils import proto_from_batched_step_result_and_action
+from mlagents_envs.communicator_objects.agent_info_action_pair_pb2 import (
+    AgentInfoActionPairProto,
+)
 
 OBS_SIZE = 1
 VIS_OBS_SIZE = (20, 20, 3)
             m_agent_id,
             action_mask,
         )
+
+
+class Record1DEnvironment(Simple1DEnvironment):
+    def __init__(
+        self, brain_names, use_discrete, step_size=0.2, num_vector=1, n_demos=30
+    ):
+        super().__init__(
+            brain_names, use_discrete, step_size=0.2, num_vector=num_vector
+        )
+        self.demonstration_protos: Dict[str, List[AgentInfoActionPairProto]] = {}
+        self.n_demos = n_demos
+        for name in self.names:
+            self.demonstration_protos[name] = []
+
+    def step(self) -> None:
+        super().step()
+        # proto_from_batched_step_result(self.step_result[name])
+        for name in self.names:
+            self.demonstration_protos[
+                name
+            ] += proto_from_batched_step_result_and_action(
+                self.step_result[name], self.action[name]
+            )
+            self.demonstration_protos[name] = self.demonstration_protos[name][
+                -self.n_demos :
+            ]

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

 from mlagents.trainers.tests.simple_test_envs import (
     Simple1DEnvironment,
     Memory1DEnvironment,
+    Record1DEnvironment,
+from mlagents.trainers.demo_loader import write_demo
+from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
+    DemonstrationMetaProto,
+)
+from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
+from mlagents_envs.communicator_objects.space_type_pb2 import discrete, continuous
 
 BRAIN_NAME = "1D"
 
             assert all(reward > success_threshold for reward in processed_rewards)
 
 
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# def test_simple_ppo(use_discrete):
+#    env = Simple1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
+#    config = generate_config(PPO_CONFIG)
+#    _check_environment_trains(env, config)
+#
+#
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# @pytest.mark.parametrize("num_visual", [1, 2])
+# def test_visual_ppo(num_visual, use_discrete):
+#    env = Simple1DEnvironment(
+#        [BRAIN_NAME],
+#        use_discrete=use_discrete,
+#        num_visual=num_visual,
+#        num_vector=0,
+#        step_size=0.2,
+#    )
+#    override_vals = {"learning_rate": 3.0e-4}
+#    config = generate_config(PPO_CONFIG, override_vals)
+#    _check_environment_trains(env, config)
+#
+#
+# @pytest.mark.parametrize("num_visual", [1, 2])
+# @pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn"])
+# def test_visual_advanced_ppo(vis_encode_type, num_visual):
+#    env = Simple1DEnvironment(
+#        [BRAIN_NAME],
+#        use_discrete=True,
+#        num_visual=num_visual,
+#        num_vector=0,
+#        step_size=0.5,
+#        vis_obs_size=(36, 36, 3),
+#    )
+#    override_vals = {
+#        "learning_rate": 3.0e-4,
+#        "vis_encode_type": vis_encode_type,
+#        "max_steps": 500,
+#        "summary_freq": 100,
+#    }
+#    config = generate_config(PPO_CONFIG, override_vals)
+#    # The number of steps is pretty small for these encoders
+#    _check_environment_trains(env, config, success_threshold=0.5)
+#
+#
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# def test_recurrent_ppo(use_discrete):
+#    env = Memory1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
+#    override_vals = {
+#        "max_steps": 3000,
+#        "batch_size": 64,
+#        "buffer_size": 128,
+#        "use_recurrent": True,
+#    }
+#    config = generate_config(PPO_CONFIG, override_vals)
+#    _check_environment_trains(env, config)
+#
+#
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# def test_simple_sac(use_discrete):
+#    env = Simple1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
+#    config = generate_config(SAC_CONFIG)
+#    _check_environment_trains(env, config)
+#
+#
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# @pytest.mark.parametrize("num_visual", [1, 2])
+# def test_visual_sac(num_visual, use_discrete):
+#    env = Simple1DEnvironment(
+#        [BRAIN_NAME],
+#        use_discrete=use_discrete,
+#        num_visual=num_visual,
+#        num_vector=0,
+#        step_size=0.2,
+#    )
+#    override_vals = {"batch_size": 16, "learning_rate": 3e-4}
+#    config = generate_config(SAC_CONFIG, override_vals)
+#    _check_environment_trains(env, config)
+#
+#
+# @pytest.mark.parametrize("num_visual", [1, 2])
+# @pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn"])
+# def test_visual_advanced_sac(vis_encode_type, num_visual):
+#    env = Simple1DEnvironment(
+#        [BRAIN_NAME],
+#        use_discrete=True,
+#        num_visual=num_visual,
+#        num_vector=0,
+#        step_size=0.5,
+#        vis_obs_size=(36, 36, 3),
+#    )
+#    override_vals = {
+#        "batch_size": 16,
+#        "learning_rate": 3.0e-4,
+#        "vis_encode_type": vis_encode_type,
+#        "buffer_init_steps": 0,
+#        "max_steps": 100,
+#    }
+#    config = generate_config(SAC_CONFIG, override_vals)
+#    # The number of steps is pretty small for these encoders
+#    _check_environment_trains(env, config, success_threshold=0.5)
+#
+#
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# def test_recurrent_sac(use_discrete):
+#    env = Memory1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
+#    override_vals = {"batch_size": 32, "use_recurrent": True, "max_steps": 2000}
+#    config = generate_config(SAC_CONFIG, override_vals)
+#    _check_environment_trains(env, config)
+#
+#
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# def test_simple_ghost(use_discrete):
+#    env = Simple1DEnvironment(
+#        [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
+#    )
+#    override_vals = {
+#        "max_steps": 2500,
+#        "self_play": {
+#            "play_against_current_self_ratio": 1.0,
+#            "save_steps": 2000,
+#            "swap_steps": 2000,
+#        },
+#    }
+#    config = generate_config(PPO_CONFIG, override_vals)
+#    _check_environment_trains(env, config)
+#
+#
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# def test_simple_ghost_fails(use_discrete):
+#    env = Simple1DEnvironment(
+#        [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
+#    )
+#    # This config should fail because the ghosted policy is never swapped with a competent policy.
+#    # Swap occurs after max step is reached.
+#    override_vals = {
+#        "max_steps": 2500,
+#        "self_play": {
+#            "play_against_current_self_ratio": 1.0,
+#            "save_steps": 2000,
+#            "swap_steps": 4000,
+#        },
+#    }
+#    config = generate_config(PPO_CONFIG, override_vals)
+#    _check_environment_trains(env, config, success_threshold=None)
+#    processed_rewards = [
+#        default_reward_processor(rewards) for rewards in env.final_rewards.values()
+#    ]
+#    success_threshold = 0.99
+#    assert any(reward > success_threshold for reward in processed_rewards) and any(
+#        reward < success_threshold for reward in processed_rewards
+#    )
+#
+#
-def test_simple_ppo(use_discrete):
-    env = Simple1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
-    config = generate_config(PPO_CONFIG)
+def test_simple_record(use_discrete):
+    env = Record1DEnvironment([BRAIN_NAME], use_discrete=use_discrete, n_demos=100)
+    override_vals = {"max_steps": 5000}
+    config = generate_config(PPO_CONFIG, override_vals)
-
-
-@pytest.mark.parametrize("use_discrete", [True, False])
-@pytest.mark.parametrize("num_visual", [1, 2])
-def test_visual_ppo(num_visual, use_discrete):
-    env = Simple1DEnvironment(
-        [BRAIN_NAME],
-        use_discrete=use_discrete,
-        num_visual=num_visual,
-        num_vector=0,
-        step_size=0.2,
+    agent_info_protos = env.demonstration_protos[BRAIN_NAME]
+    meta_data_proto = DemonstrationMetaProto()
+    brain_param_proto = BrainParametersProto(
+        vector_action_size=[1],
+        vector_action_descriptions=[""],
+        vector_action_space_type=discrete if use_discrete else continuous,
+        brain_name=BRAIN_NAME,
+        is_training=True,
-    override_vals = {"learning_rate": 3.0e-4}
-    config = generate_config(PPO_CONFIG, override_vals)
-    _check_environment_trains(env, config)
+    action_type = "Discrete" if use_discrete else "Continuous"
+    demo_path = "demos/1DTest" + action_type + ".demo"
+    write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos)
-@pytest.mark.parametrize("num_visual", [1, 2])
-@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn"])
-def test_visual_advanced_ppo(vis_encode_type, num_visual):
-    env = Simple1DEnvironment(
-        [BRAIN_NAME],
-        use_discrete=True,
-        num_visual=num_visual,
-        num_vector=0,
-        step_size=0.5,
-        vis_obs_size=(36, 36, 3),
-    )
-    override_vals = {
-        "learning_rate": 3.0e-4,
-        "vis_encode_type": vis_encode_type,
-        "max_steps": 500,
-        "summary_freq": 100,
-    }
-    config = generate_config(PPO_CONFIG, override_vals)
-    # The number of steps is pretty small for these encoders
-    _check_environment_trains(env, config, success_threshold=0.5)
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# @pytest.mark.parametrize("trainer_config", [PPO_CONFIG, SAC_CONFIG])
+# def test_gail(use_discrete, trainer_config):
+#    env = Simple1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
+#    action_type = "Discrete" if use_discrete else "Continuous"
+#    demo_path = "demos/1DTest" + action_type + ".demo"
+#    override_vals = {
+#       "max_steps": 500,
+#        "behavioral_cloning": {"demo_path": demo_path, "strength": 1.0, "steps": 2000},
+#        "reward_signals": {
+#            "gail": {
+#                "strength": 1.0,
+#                "gamma": 0.99,
+#                "encoding_size": 32,
+#                "demo_path": demo_path,
+#            }
+#        },
+#    }
+#    config = generate_config(trainer_config, override_vals)
+#    _check_environment_trains(env, config, success_threshold=0.9)
-def test_recurrent_ppo(use_discrete):
+def test_recurrent_gail_ppo(use_discrete):
+    action_type = "Discrete" if use_discrete else "Continuous"
+    demo_path = "demos/1DTest" + action_type + ".demo"
-        "max_steps": 3000,
+        "max_steps": 1000,
-    }
-    config = generate_config(PPO_CONFIG, override_vals)
-    _check_environment_trains(env, config)
-
-
-@pytest.mark.parametrize("use_discrete", [True, False])
-def test_simple_sac(use_discrete):
-    env = Simple1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
-    config = generate_config(SAC_CONFIG)
-    _check_environment_trains(env, config)
-
-
-@pytest.mark.parametrize("use_discrete", [True, False])
-@pytest.mark.parametrize("num_visual", [1, 2])
-def test_visual_sac(num_visual, use_discrete):
-    env = Simple1DEnvironment(
-        [BRAIN_NAME],
-        use_discrete=use_discrete,
-        num_visual=num_visual,
-        num_vector=0,
-        step_size=0.2,
-    )
-    override_vals = {"batch_size": 16, "learning_rate": 3e-4}
-    config = generate_config(SAC_CONFIG, override_vals)
-    _check_environment_trains(env, config)
-
-
-@pytest.mark.parametrize("num_visual", [1, 2])
-@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn"])
-def test_visual_advanced_sac(vis_encode_type, num_visual):
-    env = Simple1DEnvironment(
-        [BRAIN_NAME],
-        use_discrete=True,
-        num_visual=num_visual,
-        num_vector=0,
-        step_size=0.5,
-        vis_obs_size=(36, 36, 3),
-    )
-    override_vals = {
-        "batch_size": 16,
-        "learning_rate": 3.0e-4,
-        "vis_encode_type": vis_encode_type,
-        "buffer_init_steps": 0,
-        "max_steps": 100,
-    }
-    config = generate_config(SAC_CONFIG, override_vals)
-    # The number of steps is pretty small for these encoders
-    _check_environment_trains(env, config, success_threshold=0.5)
-
-
-@pytest.mark.parametrize("use_discrete", [True, False])
-def test_recurrent_sac(use_discrete):
-    env = Memory1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
-    override_vals = {"batch_size": 32, "use_recurrent": True, "max_steps": 2000}
-    config = generate_config(SAC_CONFIG, override_vals)
-    _check_environment_trains(env, config)
-
-
-@pytest.mark.parametrize("use_discrete", [True, False])
-def test_simple_ghost(use_discrete):
-    env = Simple1DEnvironment(
-        [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
-    )
-    override_vals = {
-        "max_steps": 2500,
-        "self_play": {
-            "play_against_current_self_ratio": 1.0,
-            "save_steps": 2000,
-            "swap_steps": 2000,
+        "behavioral_cloning": {"demo_path": demo_path, "strength": 1.0, "steps": 2000},
+        "reward_signals": {
+            "gail": {
+                "strength": 1.0,
+                "gamma": 0.99,
+                "encoding_size": 32,
+                "demo_path": demo_path,
+            }
-    _check_environment_trains(env, config)
+    _check_environment_trains(env, config, success_threshold=0.9)
-@pytest.mark.parametrize("use_discrete", [True, False])
-def test_simple_ghost_fails(use_discrete):
-    env = Simple1DEnvironment(
-        [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
-    )
-    # This config should fail because the ghosted policy is never swapped with a competent policy.
-    # Swap occurs after max step is reached.
-    override_vals = {
-        "max_steps": 2500,
-        "self_play": {
-            "play_against_current_self_ratio": 1.0,
-            "save_steps": 2000,
-            "swap_steps": 4000,
-        },
-    }
-    config = generate_config(PPO_CONFIG, override_vals)
-    _check_environment_trains(env, config, success_threshold=None)
-    processed_rewards = [
-        default_reward_processor(rewards) for rewards in env.final_rewards.values()
-    ]
-    success_threshold = 0.99
-    assert any(reward > success_threshold for reward in processed_rewards) and any(
-        reward < success_threshold for reward in processed_rewards
-    )
+# @pytest.mark.parametrize("use_discrete", [True, False])
+# def test_recurrent_sac_gail(use_discrete):
+#    env = Memory1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
+#    action_type = "Discrete" if use_discrete else "Continuous"
+#    demo_path = "demos/1DTest" + action_type + ".demo"
+#    override_vals = {"batch_size": 32, "use_recurrent": True, "max_steps": 1000,
+#        "behavioral_cloning": {"demo_path": demo_path, "strength": 1.0, "steps": 2000},
+#        "reward_signals": {
+#            "gail": {
+#                "strength": 1.0,
+#                "gamma": 0.99,
+#                "encoding_size": 128,
+#                "demo_path": demo_path,
+#            }
+#        },
+#    }
+#    config = generate_config(SAC_CONFIG, override_vals)
+#    _check_environment_trains(env, config, success_threshold=0.9)

demos/1DTestContinuous.demo

+


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#
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