import pytest from unittest import mock import yaml import numpy as np from mlagents.tf_utils import tf from mlagents.trainers.sac.models import SACModel from mlagents.trainers.sac.policy import SACPolicy from mlagents.trainers.sac.trainer import SACTrainer from mlagents.trainers.agent_processor import AgentManagerQueue from mlagents.trainers.buffer import AgentBuffer from mlagents.trainers.tests import mock_brain as mb from mlagents.trainers.tests.mock_brain import make_brain_parameters from mlagents.trainers.tests.test_trajectory import make_fake_trajectory @pytest.fixture def dummy_config(): return yaml.safe_load( """ trainer: sac batch_size: 32 buffer_size: 10240 buffer_init_steps: 0 hidden_units: 32 init_entcoef: 0.1 learning_rate: 3.0e-4 max_steps: 1024 memory_size: 8 normalize: false num_update: 1 train_interval: 1 num_layers: 1 time_horizon: 64 sequence_length: 16 summary_freq: 1000 tau: 0.005 use_recurrent: false curiosity_enc_size: 128 demo_path: None vis_encode_type: simple 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_sac_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 model_path = "testmodel" trainer_parameters["model_path"] = model_path trainer_parameters["keep_checkpoints"] = 3 trainer_parameters["use_recurrent"] = use_rnn policy = SACPolicy(0, mock_brain, trainer_parameters, False, False) return policy def test_sac_cc_policy(dummy_config): # Test evaluate tf.reset_default_graph() policy = create_sac_policy_mock( dummy_config, use_rnn=False, use_discrete=False, use_visual=False ) step = mb.create_batchedstep_from_brainparams(policy.brain, num_agents=NUM_AGENTS) run_out = policy.evaluate(step, list(step.agent_id)) assert run_out["action"].shape == (NUM_AGENTS, VECTOR_ACTION_SPACE[0]) # Test update update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain) # Mock out reward signal eval update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"] policy.update(update_buffer, num_sequences=update_buffer.num_experiences) @pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"]) def test_sac_update_reward_signals(dummy_config, discrete): # Test evaluate tf.reset_default_graph() # Add a Curiosity module dummy_config["reward_signals"]["curiosity"] = {} dummy_config["reward_signals"]["curiosity"]["strength"] = 1.0 dummy_config["reward_signals"]["curiosity"]["gamma"] = 0.99 dummy_config["reward_signals"]["curiosity"]["encoding_size"] = 128 policy = create_sac_policy_mock( dummy_config, use_rnn=False, use_discrete=discrete, use_visual=False ) # Test update, while removing PPO-specific buffer elements. update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain) # Mock out reward signal eval update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"] update_buffer["curiosity_rewards"] = update_buffer["environment_rewards"] policy.update_reward_signals( {"curiosity": update_buffer}, num_sequences=update_buffer.num_experiences ) def test_sac_dc_policy(dummy_config): # Test evaluate tf.reset_default_graph() policy = create_sac_policy_mock( dummy_config, use_rnn=False, use_discrete=True, use_visual=False ) step = mb.create_batchedstep_from_brainparams(policy.brain, num_agents=NUM_AGENTS) run_out = policy.evaluate(step, list(step.agent_id)) assert run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE)) # Test update update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain) # Mock out reward signal eval update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"] policy.update(update_buffer, num_sequences=update_buffer.num_experiences) def test_sac_visual_policy(dummy_config): # Test evaluate tf.reset_default_graph() policy = create_sac_policy_mock( dummy_config, use_rnn=False, use_discrete=True, use_visual=True ) step = mb.create_batchedstep_from_brainparams(policy.brain, num_agents=NUM_AGENTS) run_out = policy.evaluate(step, list(step.agent_id)) assert run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE)) # Test update update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain) # Mock out reward signal eval update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"] run_out = policy.update(update_buffer, num_sequences=update_buffer.num_experiences) assert type(run_out) is dict def test_sac_rnn_policy(dummy_config): # Test evaluate tf.reset_default_graph() policy = create_sac_policy_mock( dummy_config, use_rnn=True, use_discrete=True, use_visual=False ) step = mb.create_batchedstep_from_brainparams(policy.brain, num_agents=NUM_AGENTS) run_out = policy.evaluate(step, list(step.agent_id)) assert run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE)) # Test update buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain, memory_size=8) # Mock out reward signal eval buffer["extrinsic_rewards"] = buffer["environment_rewards"] update_buffer = AgentBuffer() buffer.resequence_and_append(update_buffer, training_length=policy.sequence_length) run_out = policy.update( update_buffer, num_sequences=update_buffer.num_experiences // policy.sequence_length, ) def test_sac_model_cc_vector(): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): model = SACModel( make_brain_parameters(discrete_action=False, visual_inputs=0) ) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.value, model.entropy, model.learning_rate] feed_dict = { model.batch_size: 2, model.sequence_length: 1, model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), } sess.run(run_list, feed_dict=feed_dict) def test_sac_model_cc_visual(): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): model = SACModel( make_brain_parameters(discrete_action=False, visual_inputs=2) ) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.value, model.entropy, model.learning_rate] feed_dict = { model.batch_size: 2, model.sequence_length: 1, model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.visual_in[0]: np.ones([2, 40, 30, 3], dtype=np.float32), model.visual_in[1]: np.ones([2, 40, 30, 3], dtype=np.float32), } sess.run(run_list, feed_dict=feed_dict) def test_sac_model_dc_visual(): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): model = SACModel( make_brain_parameters(discrete_action=True, visual_inputs=2) ) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.value, model.entropy, model.learning_rate] feed_dict = { model.batch_size: 2, model.sequence_length: 1, model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.visual_in[0]: np.ones([2, 40, 30, 3], dtype=np.float32), model.visual_in[1]: np.ones([2, 40, 30, 3], dtype=np.float32), model.action_masks: np.ones([2, 2], dtype=np.float32), } sess.run(run_list, feed_dict=feed_dict) def test_sac_model_dc_vector(): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): model = SACModel( make_brain_parameters(discrete_action=True, visual_inputs=0) ) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.value, model.entropy, model.learning_rate] feed_dict = { model.batch_size: 2, model.sequence_length: 1, model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.action_masks: np.ones([2, 2], dtype=np.float32), } sess.run(run_list, feed_dict=feed_dict) def test_sac_model_dc_vector_rnn(): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): memory_size = 128 model = SACModel( make_brain_parameters(discrete_action=True, visual_inputs=0), use_recurrent=True, m_size=memory_size, ) init = tf.global_variables_initializer() sess.run(init) run_list = [ model.output, model.all_log_probs, model.value, model.entropy, model.learning_rate, model.memory_out, ] feed_dict = { model.batch_size: 1, model.sequence_length: 2, model.prev_action: [[0], [0]], model.memory_in: np.zeros((1, memory_size), dtype=np.float32), model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.action_masks: np.ones([1, 2], dtype=np.float32), } sess.run(run_list, feed_dict=feed_dict) def test_sac_model_cc_vector_rnn(): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): memory_size = 128 model = SACModel( make_brain_parameters(discrete_action=False, visual_inputs=0), use_recurrent=True, m_size=memory_size, ) init = tf.global_variables_initializer() sess.run(init) run_list = [ model.output, model.all_log_probs, model.value, model.entropy, model.learning_rate, model.memory_out, ] feed_dict = { model.batch_size: 1, model.sequence_length: 2, model.memory_in: np.zeros((1, memory_size), dtype=np.float32), model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), } sess.run(run_list, feed_dict=feed_dict) def test_sac_save_load_buffer(tmpdir, dummy_config): mock_brain = mb.setup_mock_brain( False, False, vector_action_space=VECTOR_ACTION_SPACE, vector_obs_space=VECTOR_OBS_SPACE, discrete_action_space=DISCRETE_ACTION_SPACE, ) trainer_params = dummy_config trainer_params["summary_path"] = str(tmpdir) trainer_params["model_path"] = str(tmpdir) trainer_params["save_replay_buffer"] = True trainer = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, False, 0, 0) policy = trainer.create_policy(mock_brain) trainer.add_policy(mock_brain.brain_name, policy) trainer.update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain) buffer_len = trainer.update_buffer.num_experiences trainer.save_model(mock_brain.brain_name) # Wipe Trainer and try to load trainer2 = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, True, 0, 0) policy = trainer2.create_policy(mock_brain) trainer2.add_policy(mock_brain.brain_name, policy) assert trainer2.update_buffer.num_experiences == buffer_len def test_add_get_policy(dummy_config): brain_params = make_brain_parameters( discrete_action=False, visual_inputs=0, vec_obs_size=6 ) dummy_config["summary_path"] = "./summaries/test_trainer_summary" dummy_config["model_path"] = "./models/test_trainer_models/TestModel" trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0") policy = mock.Mock(spec=SACPolicy) policy.get_current_step.return_value = 2000 trainer.add_policy(brain_params.brain_name, policy) assert trainer.get_policy(brain_params.brain_name) == policy # Make sure the summary steps were loaded properly assert trainer.get_step == 2000 assert trainer.next_summary_step > 2000 # Test incorrect class of policy policy = mock.Mock() with pytest.raises(RuntimeError): trainer.add_policy(brain_params, policy) def test_process_trajectory(dummy_config): brain_params = make_brain_parameters( discrete_action=False, visual_inputs=0, vec_obs_size=6 ) dummy_config["summary_path"] = "./summaries/test_trainer_summary" dummy_config["model_path"] = "./models/test_trainer_models/TestModel" trainer = SACTrainer(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) trajectory = make_fake_trajectory( length=15, max_step_complete=True, vec_obs_size=6, 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 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=15, max_step_complete=False, vec_obs_size=6, 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 if __name__ == "__main__": pytest.main()