ghost trainer tests

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

         self.trainer.save_model()
 
     def create_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+        self,
+        parsed_behavior_id: BehaviorIdentifiers,
+        behavior_spec: BehaviorSpec,
+        create_graph: bool = False,
     ) -> Policy:
         """
         Creates policy with the wrapped trainer's create_policy function
         wrapped trainer to be trained.
         """
-        policy = self.trainer.create_policy(parsed_behavior_id, behavior_spec)
-        policy.create_tf_graph()
+        policy = self.trainer.create_policy(
+            parsed_behavior_id, behavior_spec, create_graph=True
+        )
-        policy.init_load_weights()
         team_id = parsed_behavior_id.team_id
         self.controller.subscribe_team_id(team_id, self)
 
                 parsed_behavior_id, behavior_spec
             )
             self.trainer.add_policy(parsed_behavior_id, internal_trainer_policy)
-            internal_trainer_policy.init_load_weights()
             self.current_policy_snapshot[
                 parsed_behavior_id.brain_name
             ] = internal_trainer_policy.get_weights()

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

             self.trainable_variables += tf.get_collection(
                 tf.GraphKeys.TRAINABLE_VARIABLES, scope="lstm"
             )  # LSTMs need to be root scope for Barracuda export
+            # Create assignment ops for Ghost Trainer
+            self.init_load_weights()
 
         self.inference_dict = {
             "action": self.output,

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

         pass
 
     def get_weights(self) -> List[np.ndarray]:
-        return []
+        return self.actor_critic.state_dict()
 
     def get_modules(self):
         return {"Policy": self.actor_critic, "global_step": self.global_step}

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

         return True
 
     def create_tf_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+        self,
+        parsed_behavior_id: BehaviorIdentifiers,
+        behavior_spec: BehaviorSpec,
+        create_graph: bool = False,
     ) -> TFPolicy:
         """
         Creates a PPO policy to trainers list of policies.
             behavior_spec,
             self.trainer_settings,
             condition_sigma_on_obs=False,  # Faster training for PPO
+            create_tf_graph=create_graph,
         )
         return policy
 

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

         return False
 
     def create_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+        self,
+        parsed_behavior_id: BehaviorIdentifiers,
+        behavior_spec: BehaviorSpec,
+        create_graph: bool = False,
     ) -> Policy:
         if self.framework == FrameworkType.PYTORCH and TorchPolicy is None:
             raise UnityTrainerException(
             return self.create_torch_policy(parsed_behavior_id, behavior_spec)
         else:
-            return self.create_tf_policy(parsed_behavior_id, behavior_spec)
+            return self.create_tf_policy(
+                parsed_behavior_id, behavior_spec, create_graph=create_graph
+            )
 
     @abc.abstractmethod
     def create_torch_policy(
 
     @abc.abstractmethod
     def create_tf_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+        self,
+        parsed_behavior_id: BehaviorIdentifiers,
+        behavior_spec: BehaviorSpec,
+        create_graph: bool = False,
     ) -> TFPolicy:
         """
         Create a Policy object that uses the TensorFlow backend.

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

 
     @abc.abstractmethod
     def create_policy(
-        self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec
+        self,
+        parsed_behavior_id: BehaviorIdentifiers,
+        behavior_spec: BehaviorSpec,
+        create_graph: bool = False,
     ) -> Policy:
         """
         Creates policy

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

+import pytest
+
+import numpy as np
+
+from mlagents.trainers.ghost.trainer import GhostTrainer
+from mlagents.trainers.ghost.controller import GhostController
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
+from mlagents.trainers.ppo.trainer import PPOTrainer
+from mlagents.trainers.agent_processor import AgentManagerQueue
+from mlagents.trainers.tests import mock_brain as mb
+from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
+from mlagents.trainers.settings import TrainerSettings, SelfPlaySettings, FrameworkType
+
+
+@pytest.fixture
+def dummy_config():
+    return TrainerSettings(
+        self_play=SelfPlaySettings(), framework=FrameworkType.PYTORCH
+    )
+
+
+VECTOR_ACTION_SPACE = 1
+VECTOR_OBS_SPACE = 8
+DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
+BUFFER_INIT_SAMPLES = 513
+NUM_AGENTS = 12
+
+
+@pytest.mark.parametrize("use_discrete", [True, False])
+def test_load_and_set(dummy_config, use_discrete):
+    mock_specs = mb.setup_test_behavior_specs(
+        use_discrete,
+        False,
+        vector_action_space=DISCRETE_ACTION_SPACE
+        if use_discrete
+        else VECTOR_ACTION_SPACE,
+        vector_obs_space=VECTOR_OBS_SPACE,
+    )
+
+    trainer_params = dummy_config
+    trainer = PPOTrainer("test", 0, trainer_params, True, False, 0, "0")
+    trainer.seed = 1
+    policy = trainer.create_policy("test", mock_specs, create_graph=True)
+    trainer.seed = 20  # otherwise graphs are the same
+    to_load_policy = trainer.create_policy("test", mock_specs, create_graph=True)
+
+    weights = policy.get_weights()
+    load_weights = to_load_policy.get_weights()
+    try:
+        for w, lw in zip(weights, load_weights):
+            np.testing.assert_array_equal(w, lw)
+    except AssertionError:
+        pass
+
+    to_load_policy.load_weights(weights)
+    load_weights = to_load_policy.get_weights()
+
+    for w, lw in zip(weights, load_weights):
+        np.testing.assert_array_equal(w, lw)
+
+
+def test_process_trajectory(dummy_config):
+    mock_specs = mb.setup_test_behavior_specs(
+        True, False, vector_action_space=[2], vector_obs_space=1
+    )
+    behavior_id_team0 = "test_brain?team=0"
+    behavior_id_team1 = "test_brain?team=1"
+    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
+
+    ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, "0")
+    controller = GhostController(100)
+    trainer = GhostTrainer(
+        ppo_trainer, brain_name, controller, 0, dummy_config, True, "0"
+    )
+
+    # first policy encountered becomes policy trained by wrapped PPO
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
+    policy = trainer.create_policy(parsed_behavior_id0, mock_specs)
+    trainer.add_policy(parsed_behavior_id0, policy)
+    trajectory_queue0 = AgentManagerQueue(behavior_id_team0)
+    trainer.subscribe_trajectory_queue(trajectory_queue0)
+
+    # Ghost trainer should ignore this queue because off policy
+    parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team1)
+    policy = trainer.create_policy(parsed_behavior_id1, mock_specs)
+    trainer.add_policy(parsed_behavior_id1, policy)
+    trajectory_queue1 = AgentManagerQueue(behavior_id_team1)
+    trainer.subscribe_trajectory_queue(trajectory_queue1)
+
+    time_horizon = 15
+    trajectory = make_fake_trajectory(
+        length=time_horizon,
+        max_step_complete=True,
+        observation_shapes=[(1,)],
+        action_space=[2],
+    )
+    trajectory_queue0.put(trajectory)
+    trainer.advance()
+
+    # Check that trainer put trajectory in update buffer
+    assert trainer.trainer.update_buffer.num_experiences == 15
+
+    trajectory_queue1.put(trajectory)
+    trainer.advance()
+
+    # Check that ghost trainer ignored off policy queue
+    assert trainer.trainer.update_buffer.num_experiences == 15
+    # Check that it emptied the queue
+    assert trajectory_queue1.empty()
+
+
+def test_publish_queue(dummy_config):
+    mock_specs = mb.setup_test_behavior_specs(
+        True, False, vector_action_space=[1], vector_obs_space=8
+    )
+
+    behavior_id_team0 = "test_brain?team=0"
+    behavior_id_team1 = "test_brain?team=1"
+
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
+
+    brain_name = parsed_behavior_id0.brain_name
+
+    ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, "0")
+    controller = GhostController(100)
+    trainer = GhostTrainer(
+        ppo_trainer, brain_name, controller, 0, dummy_config, True, "0"
+    )
+
+    # First policy encountered becomes policy trained by wrapped PPO
+    # This queue should remain empty after swap snapshot
+    policy = trainer.create_policy(parsed_behavior_id0, mock_specs)
+    trainer.add_policy(parsed_behavior_id0, policy)
+    policy_queue0 = AgentManagerQueue(behavior_id_team0)
+    trainer.publish_policy_queue(policy_queue0)
+
+    # Ghost trainer should use this queue for ghost policy swap
+    parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team1)
+    policy = trainer.create_policy(parsed_behavior_id1, mock_specs)
+    trainer.add_policy(parsed_behavior_id1, policy)
+    policy_queue1 = AgentManagerQueue(behavior_id_team1)
+    trainer.publish_policy_queue(policy_queue1)
+
+    # check ghost trainer swap pushes to ghost queue and not trainer
+    assert policy_queue0.empty() and policy_queue1.empty()
+    trainer._swap_snapshots()
+    assert policy_queue0.empty() and not policy_queue1.empty()
+    # clear
+    policy_queue1.get_nowait()
+
+    mock_specs = mb.setup_test_behavior_specs(
+        False,
+        False,
+        vector_action_space=VECTOR_ACTION_SPACE,
+        vector_obs_space=VECTOR_OBS_SPACE,
+    )
+
+    buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, mock_specs)
+    # Mock out reward signal eval
+    buffer["extrinsic_rewards"] = buffer["environment_rewards"]
+    buffer["extrinsic_returns"] = buffer["environment_rewards"]
+    buffer["extrinsic_value_estimates"] = buffer["environment_rewards"]
+    buffer["curiosity_rewards"] = buffer["environment_rewards"]
+    buffer["curiosity_returns"] = buffer["environment_rewards"]
+    buffer["curiosity_value_estimates"] = buffer["environment_rewards"]
+    buffer["advantages"] = buffer["environment_rewards"]
+    trainer.trainer.update_buffer = buffer
+
+    # when ghost trainer advance and wrapped trainer buffers full
+    # the wrapped trainer pushes updated policy to correct queue
+    assert policy_queue0.empty() and policy_queue1.empty()
+    trainer.advance()
+    assert not policy_queue0.empty() and policy_queue1.empty()
+
+
+if __name__ == "__main__":
+    pytest.main()