def test_process_trajectory(dummy_config): brain_params = BrainParameters( brain_name="test_brain", vector_observation_space_size=1, camera_resolutions=[], vector_action_space_size=[2], vector_action_descriptions=[], vector_action_space_type=0, ) dummy_config["summary_path"] = "./summaries/test_trainer_summary" dummy_config["model_path"] = "./models/test_trainer_models/TestModel" trainer = PPOTrainer(brain_params, 0, dummy_config, True, False, 0, "0") policy = trainer.create_policy(brain_params) trainer.add_policy(brain_params.brain_name, policy) trajectory_queue = AgentManagerQueue("testbrain") trainer.subscribe_trajectory_queue(trajectory_queue) time_horizon = 15 trajectory = make_fake_trajectory( length=time_horizon, max_step_complete=True, vec_obs_size=1, num_vis_obs=0, action_space=[2], ) trajectory_queue.put(trajectory) trainer.advance() # Check that trainer put trajectory in update buffer assert trainer.update_buffer.num_experiences == 15 # Check that GAE worked assert ( "advantages" in trainer.update_buffer and "discounted_returns" in trainer.update_buffer ) # Check that the stats are being collected as episode isn't complete for reward in trainer.collected_rewards.values(): for agent in reward.values(): assert agent > 0 # Add a terminal trajectory trajectory = make_fake_trajectory( length=time_horizon + 1, max_step_complete=False, vec_obs_size=1, num_vis_obs=0, action_space=[2], ) trajectory_queue.put(trajectory) trainer.advance() # Check that the stats are reset as episode is finished for reward in trainer.collected_rewards.values(): for agent in reward.values(): assert agent == 0 assert trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").num > 0
def test_process_trajectory(dummy_config): behavior_spec = mb.setup_test_behavior_specs( True, False, vector_action_space=DISCRETE_ACTION_SPACE, vector_obs_space=VECTOR_OBS_SPACE, ) mock_brain_name = "MockBrain" behavior_id = BehaviorIdentifiers.from_name_behavior_id(mock_brain_name) trainer = PPOTrainer("test_brain", 0, dummy_config, True, False, 0, "0") policy = trainer.create_policy(behavior_id, behavior_spec) trainer.add_policy(behavior_id, policy) trajectory_queue = AgentManagerQueue("testbrain") trainer.subscribe_trajectory_queue(trajectory_queue) time_horizon = 15 trajectory = make_fake_trajectory( length=time_horizon, observation_shapes=behavior_spec.observation_shapes, max_step_complete=True, action_space=[2], ) trajectory_queue.put(trajectory) trainer.advance() # Check that trainer put trajectory in update buffer assert trainer.update_buffer.num_experiences == 15 # Check that GAE worked assert ( "advantages" in trainer.update_buffer and "discounted_returns" in trainer.update_buffer ) # Check that the stats are being collected as episode isn't complete for reward in trainer.collected_rewards.values(): for agent in reward.values(): assert agent > 0 # Add a terminal trajectory trajectory = make_fake_trajectory( length=time_horizon + 1, max_step_complete=False, observation_shapes=behavior_spec.observation_shapes, action_space=[2], ) trajectory_queue.put(trajectory) trainer.advance() # Check that the stats are reset as episode is finished for reward in trainer.collected_rewards.values(): for agent in reward.values(): assert agent == 0 assert trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").num > 0