import os from unittest import mock import pytest import mlagents.trainers.tests.mock_brain as mb from mlagents.trainers.policy.checkpoint_manager import NNCheckpoint from mlagents.trainers.trainer.rl_trainer import RLTrainer from mlagents.trainers.tests.test_buffer import construct_fake_buffer from mlagents.trainers.agent_processor import AgentManagerQueue from mlagents.trainers.settings import TrainerSettings # Add concrete implementations of abstract methods class FakeTrainer(RLTrainer): def set_is_policy_updating(self, is_updating): self.update_policy = is_updating def get_policy(self, name_behavior_id): return mock.Mock() def _is_ready_update(self): return True def _update_policy(self): return self.update_policy def add_policy(self, mock_behavior_id, mock_policy): def checkpoint_path(brain_name, step): return os.path.join(self.saver.model_path, f"{brain_name}-{step}") self.policies[mock_behavior_id] = mock_policy mock_saver = mock.Mock() mock_saver.model_path = self.artifact_path mock_saver.save_checkpoint.side_effect = checkpoint_path self.saver = mock_saver def create_tf_policy(self, parsed_behavior_id, behavior_spec): return mock.Mock() def create_torch_policy(self, parsed_behavior_id, behavior_spec): return mock.Mock() def _process_trajectory(self, trajectory): super()._process_trajectory(trajectory) def create_rl_trainer(): trainer = FakeTrainer( "test_trainer", TrainerSettings(max_steps=100, checkpoint_interval=10, summary_freq=20), True, False, "mock_model_path", 0, ) trainer.set_is_policy_updating(True) return trainer def test_rl_trainer(): trainer = create_rl_trainer() agent_id = "0" trainer.collected_rewards["extrinsic"] = {agent_id: 3} # Test end episode trainer.end_episode() for rewards in trainer.collected_rewards.values(): for agent_id in rewards: assert rewards[agent_id] == 0 def test_clear_update_buffer(): trainer = create_rl_trainer() trainer.update_buffer = construct_fake_buffer(0) trainer._clear_update_buffer() for _, arr in trainer.update_buffer.items(): assert len(arr) == 0 @mock.patch("mlagents.trainers.trainer.trainer.Trainer.save_model") @mock.patch("mlagents.trainers.trainer.rl_trainer.RLTrainer._clear_update_buffer") def test_advance(mocked_clear_update_buffer, mocked_save_model): trainer = create_rl_trainer() mock_policy = mock.Mock() trainer.add_policy("TestBrain", mock_policy) trajectory_queue = AgentManagerQueue("testbrain") policy_queue = AgentManagerQueue("testbrain") trainer.subscribe_trajectory_queue(trajectory_queue) trainer.publish_policy_queue(policy_queue) time_horizon = 10 trajectory = mb.make_fake_trajectory( length=time_horizon, observation_shapes=[(1,)], max_step_complete=True, action_space=[2], ) trajectory_queue.put(trajectory) trainer.advance() policy_queue.get_nowait() # Check that get_step is correct assert trainer.get_step == time_horizon # Check that we can turn off the trainer and that the buffer is cleared for _ in range(0, 5): trajectory_queue.put(trajectory) trainer.advance() # Check that there is stuff in the policy queue policy_queue.get_nowait() # Check that if the policy doesn't update, we don't push it to the queue trainer.set_is_policy_updating(False) for _ in range(0, 10): trajectory_queue.put(trajectory) trainer.advance() # Check that there nothing in the policy queue with pytest.raises(AgentManagerQueue.Empty): policy_queue.get_nowait() # Check that the buffer has been cleared assert not trainer.should_still_train assert mocked_clear_update_buffer.call_count > 0 assert mocked_save_model.call_count == 0 @mock.patch("mlagents.trainers.trainer.trainer.StatsReporter.write_stats") @mock.patch("mlagents.trainers.trainer.rl_trainer.NNCheckpointManager.add_checkpoint") def test_summary_checkpoint(mock_add_checkpoint, mock_write_summary): trainer = create_rl_trainer() mock_policy = mock.Mock() trainer.add_policy("TestBrain", mock_policy) trajectory_queue = AgentManagerQueue("testbrain") policy_queue = AgentManagerQueue("testbrain") trainer.subscribe_trajectory_queue(trajectory_queue) trainer.publish_policy_queue(policy_queue) time_horizon = 10 summary_freq = trainer.trainer_settings.summary_freq checkpoint_interval = trainer.trainer_settings.checkpoint_interval trajectory = mb.make_fake_trajectory( length=time_horizon, observation_shapes=[(1,)], max_step_complete=True, action_space=[2], ) # Check that we can turn off the trainer and that the buffer is cleared num_trajectories = 5 for _ in range(0, num_trajectories): trajectory_queue.put(trajectory) trainer.advance() # Check that there is stuff in the policy queue policy_queue.get_nowait() # Check that we have called write_summary the appropriate number of times calls = [ mock.call(step) for step in range(summary_freq, num_trajectories * time_horizon, summary_freq) ] mock_write_summary.assert_has_calls(calls, any_order=True) checkpoint_range = range( checkpoint_interval, num_trajectories * time_horizon, checkpoint_interval ) calls = [mock.call(trainer.brain_name, step) for step in checkpoint_range] trainer.saver.save_checkpoint.assert_has_calls(calls, any_order=True) add_checkpoint_calls = [ mock.call( trainer.brain_name, NNCheckpoint( step, f"{trainer.saver.model_path}/{trainer.brain_name}-{step}.nn", None, mock.ANY, ), trainer.trainer_settings.keep_checkpoints, ) for step in checkpoint_range ] mock_add_checkpoint.assert_has_calls(add_checkpoint_calls)