import pytest import numpy as np from mlagents.tf_utils import tf import yaml from mlagents.trainers.policy.nn_policy import NNPolicy from mlagents.trainers.models import EncoderType, ModelUtils from mlagents.trainers.exception import UnityTrainerException from mlagents.trainers.brain import BrainParameters, CameraResolution from mlagents.trainers.tests import mock_brain as mb from mlagents.trainers.tests.test_trajectory import make_fake_trajectory @pytest.fixture def dummy_config(): return yaml.safe_load( """ trainer: ppo batch_size: 32 beta: 5.0e-3 buffer_size: 512 epsilon: 0.2 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 normalize: true memory_size: 8 curiosity_strength: 0.0 curiosity_enc_size: 1 summary_path: test model_path: test reward_signals: extrinsic: strength: 1.0 gamma: 0.99 """ ) VECTOR_ACTION_SPACE = [2] VECTOR_OBS_SPACE = 8 DISCRETE_ACTION_SPACE = [3, 3, 3, 2] BUFFER_INIT_SAMPLES = 32 NUM_AGENTS = 12 def create_policy_mock(dummy_config, use_rnn, use_discrete, use_visual): mock_brain = mb.setup_mock_brain( use_discrete, use_visual, vector_action_space=VECTOR_ACTION_SPACE, vector_obs_space=VECTOR_OBS_SPACE, discrete_action_space=DISCRETE_ACTION_SPACE, ) trainer_parameters = dummy_config trainer_parameters["keep_checkpoints"] = 3 trainer_parameters["use_recurrent"] = use_rnn policy = NNPolicy(0, mock_brain, trainer_parameters, False, False) return policy @pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"]) @pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"]) @pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"]) def test_policy_evaluate(dummy_config, rnn, visual, discrete): # Test evaluate tf.reset_default_graph() policy = create_policy_mock( dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual ) step = mb.create_batchedstep_from_brainparams(policy.brain, num_agents=NUM_AGENTS) run_out = policy.evaluate(step, list(step.agent_id)) if discrete: run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE)) else: assert run_out["action"].shape == (NUM_AGENTS, VECTOR_ACTION_SPACE[0]) def test_normalization(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" time_horizon = 6 trajectory = make_fake_trajectory( length=time_horizon, max_step_complete=True, vec_obs_size=1, num_vis_obs=0, action_space=[2], ) # Change half of the obs to 0 for i in range(3): trajectory.steps[i].obs[0] = np.zeros(1, dtype=np.float32) policy = policy = NNPolicy(0, brain_params, dummy_config, False, False) trajectory_buffer = trajectory.to_agentbuffer() policy.update_normalization(trajectory_buffer["vector_obs"]) # Check that the running mean and variance is correct steps, mean, variance = policy.sess.run( [policy.normalization_steps, policy.running_mean, policy.running_variance] ) assert steps == 6 assert mean[0] == 0.5 # Note: variance is divided by number of steps, and initialized to 1 to avoid # divide by 0. The right answer is 0.25 assert (variance[0] - 1) / steps == 0.25 # Make another update, this time with all 1's time_horizon = 10 trajectory = make_fake_trajectory( length=time_horizon, max_step_complete=True, vec_obs_size=1, num_vis_obs=0, action_space=[2], ) trajectory_buffer = trajectory.to_agentbuffer() policy.update_normalization(trajectory_buffer["vector_obs"]) # Check that the running mean and variance is correct steps, mean, variance = policy.sess.run( [policy.normalization_steps, policy.running_mean, policy.running_variance] ) assert steps == 16 assert mean[0] == 0.8125 assert (variance[0] - 1) / steps == pytest.approx(0.152, abs=0.01) def test_min_visual_size(): # Make sure each EncoderType has an entry in MIS_RESOLUTION_FOR_ENCODER assert set(ModelUtils.MIN_RESOLUTION_FOR_ENCODER.keys()) == set(EncoderType) for encoder_type in EncoderType: with tf.Graph().as_default(): good_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type] good_res = CameraResolution( width=good_size, height=good_size, num_channels=3 ) vis_input = ModelUtils.create_visual_input(good_res, "test_min_visual_size") ModelUtils._check_resolution_for_encoder(vis_input, encoder_type) enc_func = ModelUtils.get_encoder_for_type(encoder_type) enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False) # Anything under the min size should raise an exception. If not, decrease the min size! with pytest.raises(Exception): with tf.Graph().as_default(): bad_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type] - 1 bad_res = CameraResolution( width=bad_size, height=bad_size, num_channels=3 ) vis_input = ModelUtils.create_visual_input( bad_res, "test_min_visual_size" ) with pytest.raises(UnityTrainerException): # Make sure we'd hit a friendly error during model setup time. ModelUtils._check_resolution_for_encoder(vis_input, encoder_type) enc_func = ModelUtils.get_encoder_for_type(encoder_type) enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False) if __name__ == "__main__": pytest.main()