import pytest import yaml import io from unittest.mock import patch from mlagents.trainers import trainer_util from mlagents.trainers.trainer_util import load_config, _load_config from mlagents.trainers.ppo.trainer import PPOTrainer from mlagents.trainers.exception import TrainerConfigError from mlagents.trainers.brain import BrainParameters @pytest.fixture def dummy_config(): return yaml.safe_load( """ default: trainer: ppo batch_size: 32 beta: 5.0e-3 buffer_size: 512 epsilon: 0.2 gamma: 0.99 hidden_units: 128 lambd: 0.95 learning_rate: 3.0e-4 max_steps: 5.0e4 normalize: true num_epoch: 5 num_layers: 2 time_horizon: 64 sequence_length: 64 summary_freq: 1000 use_recurrent: false memory_size: 8 use_curiosity: false curiosity_strength: 0.0 curiosity_enc_size: 1 reward_signals: extrinsic: strength: 1.0 gamma: 0.99 """ ) @pytest.fixture def dummy_config_with_override(dummy_config): base = dummy_config base["testbrain"] = {} base["testbrain"]["normalize"] = False return base @pytest.fixture def dummy_bad_config(): return yaml.safe_load( """ default: trainer: incorrect_trainer brain_to_imitate: ExpertBrain batches_per_epoch: 16 batch_size: 32 beta: 5.0e-3 buffer_size: 512 epsilon: 0.2 gamma: 0.99 hidden_units: 128 lambd: 0.95 learning_rate: 3.0e-4 max_steps: 5.0e4 normalize: true num_epoch: 5 num_layers: 2 time_horizon: 64 sequence_length: 64 summary_freq: 1000 use_recurrent: false memory_size: 8 """ ) @patch("mlagents.trainers.brain.BrainParameters") def test_initialize_trainer_parameters_override_defaults( BrainParametersMock, dummy_config_with_override ): summaries_dir = "test_dir" run_id = "testrun" model_path = "model_dir" keep_checkpoints = 1 train_model = True load_model = False seed = 11 expected_reward_buff_cap = 1 base_config = dummy_config_with_override expected_config = base_config["default"] expected_config["summary_path"] = f"{run_id}_testbrain" expected_config["model_path"] = model_path + "/testbrain" expected_config["keep_checkpoints"] = keep_checkpoints # Override value from specific brain config expected_config["normalize"] = False brain_params_mock = BrainParametersMock() BrainParametersMock.return_value.brain_name = "testbrain" external_brains = {"testbrain": brain_params_mock} def mock_constructor( self, brain, reward_buff_cap, trainer_parameters, training, load, seed, run_id, multi_gpu, ): assert brain == brain_params_mock.brain_name assert trainer_parameters == expected_config assert reward_buff_cap == expected_reward_buff_cap assert training == train_model assert load == load_model assert seed == seed assert run_id == run_id assert multi_gpu == multi_gpu with patch.object(PPOTrainer, "__init__", mock_constructor): trainer_factory = trainer_util.TrainerFactory( trainer_config=base_config, summaries_dir=summaries_dir, run_id=run_id, model_path=model_path, keep_checkpoints=keep_checkpoints, train_model=train_model, load_model=load_model, seed=seed, ) trainers = {} for _, brain_parameters in external_brains.items(): trainers["testbrain"] = trainer_factory.generate( brain_parameters.brain_name ) assert "testbrain" in trainers assert isinstance(trainers["testbrain"], PPOTrainer) @patch("mlagents.trainers.brain.BrainParameters") def test_initialize_ppo_trainer(BrainParametersMock, dummy_config): brain_params_mock = BrainParametersMock() BrainParametersMock.return_value.brain_name = "testbrain" external_brains = {"testbrain": BrainParametersMock()} summaries_dir = "test_dir" run_id = "testrun" model_path = "model_dir" keep_checkpoints = 1 train_model = True load_model = False seed = 11 expected_reward_buff_cap = 1 base_config = dummy_config expected_config = base_config["default"] expected_config["summary_path"] = f"{run_id}_testbrain" expected_config["model_path"] = model_path + "/testbrain" expected_config["keep_checkpoints"] = keep_checkpoints def mock_constructor( self, brain, reward_buff_cap, trainer_parameters, training, load, seed, run_id, multi_gpu, ): assert brain == brain_params_mock.brain_name assert trainer_parameters == expected_config assert reward_buff_cap == expected_reward_buff_cap assert training == train_model assert load == load_model assert seed == seed assert run_id == run_id assert multi_gpu == multi_gpu with patch.object(PPOTrainer, "__init__", mock_constructor): trainer_factory = trainer_util.TrainerFactory( trainer_config=base_config, summaries_dir=summaries_dir, run_id=run_id, model_path=model_path, keep_checkpoints=keep_checkpoints, train_model=train_model, load_model=load_model, seed=seed, ) trainers = {} for brain_name, brain_parameters in external_brains.items(): trainers[brain_name] = trainer_factory.generate(brain_parameters.brain_name) assert "testbrain" in trainers assert isinstance(trainers["testbrain"], PPOTrainer) @patch("mlagents.trainers.brain.BrainParameters") def test_initialize_invalid_trainer_raises_exception( BrainParametersMock, dummy_bad_config ): summaries_dir = "test_dir" run_id = "testrun" model_path = "model_dir" keep_checkpoints = 1 train_model = True load_model = False seed = 11 bad_config = dummy_bad_config BrainParametersMock.return_value.brain_name = "testbrain" external_brains = {"testbrain": BrainParametersMock()} with pytest.raises(TrainerConfigError): trainer_factory = trainer_util.TrainerFactory( trainer_config=bad_config, summaries_dir=summaries_dir, run_id=run_id, model_path=model_path, keep_checkpoints=keep_checkpoints, train_model=train_model, load_model=load_model, seed=seed, ) trainers = {} for brain_name, brain_parameters in external_brains.items(): trainers[brain_name] = trainer_factory.generate(brain_parameters.brain_name) def test_handles_no_default_section(dummy_config): """ Make sure the trainer setup handles a missing "default" in the config. """ brain_name = "testbrain" no_default_config = {brain_name: dummy_config["default"]} brain_parameters = BrainParameters( brain_name=brain_name, vector_observation_space_size=1, camera_resolutions=[], vector_action_space_size=[2], vector_action_descriptions=[], vector_action_space_type=0, ) trainer_factory = trainer_util.TrainerFactory( trainer_config=no_default_config, summaries_dir="test_dir", run_id="testrun", model_path="model_dir", keep_checkpoints=1, train_model=True, load_model=False, seed=42, ) trainer_factory.generate(brain_parameters.brain_name) def test_raise_if_no_config_for_brain(dummy_config): """ Make sure the trainer setup raises a friendlier exception if both "default" and the brain name are missing from the config. """ brain_name = "testbrain" bad_config = {"some_other_brain": dummy_config["default"]} brain_parameters = BrainParameters( brain_name=brain_name, vector_observation_space_size=1, camera_resolutions=[], vector_action_space_size=[2], vector_action_descriptions=[], vector_action_space_type=0, ) trainer_factory = trainer_util.TrainerFactory( trainer_config=bad_config, summaries_dir="test_dir", run_id="testrun", model_path="model_dir", keep_checkpoints=1, train_model=True, load_model=False, seed=42, ) with pytest.raises(TrainerConfigError): trainer_factory.generate(brain_parameters) def test_load_config_missing_file(): with pytest.raises(TrainerConfigError): load_config("thisFileDefinitelyDoesNotExist.yaml") def test_load_config_valid_yaml(): file_contents = """ this: - is fine """ fp = io.StringIO(file_contents) res = _load_config(fp) assert res == {"this": ["is fine"]} def test_load_config_invalid_yaml(): file_contents = """ you: - will - not - parse """ with pytest.raises(TrainerConfigError): fp = io.StringIO(file_contents) _load_config(fp)