import pytest from unittest import mock import copy from mlagents.tf_utils import tf from mlagents.trainers.sac.trainer import SACTrainer from mlagents.trainers.sac.optimizer import SACOptimizer from mlagents.trainers.policy.nn_policy import NNPolicy from mlagents.trainers.agent_processor import AgentManagerQueue 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 from mlagents.trainers.tests.test_simple_rl import SAC_CONFIG from mlagents.trainers.settings import NetworkSettings from mlagents.trainers.tests.test_reward_signals import ( # noqa: F401; pylint: disable=unused-variable curiosity_dummy_config, ) @pytest.fixture def dummy_config(): return copy.deepcopy(SAC_CONFIG) VECTOR_ACTION_SPACE = [2] VECTOR_OBS_SPACE = 8 DISCRETE_ACTION_SPACE = [3, 3, 3, 2] BUFFER_INIT_SAMPLES = 64 NUM_AGENTS = 12 def create_sac_optimizer_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_settings = dummy_config trainer_settings.network_settings.memory = ( NetworkSettings.MemorySettings(sequence_length=16, memory_size=10) if use_rnn else None ) policy = NNPolicy( 0, mock_brain, trainer_settings, False, False, create_tf_graph=False ) optimizer = SACOptimizer(policy, trainer_settings) return optimizer @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_sac_optimizer_update(dummy_config, rnn, visual, discrete): # Test evaluate tf.reset_default_graph() optimizer = create_sac_optimizer_mock( dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual ) # Test update update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, optimizer.policy.brain) # Mock out reward signal eval update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"] optimizer.update( update_buffer, num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length, ) @pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"]) def test_sac_update_reward_signals( dummy_config, curiosity_dummy_config, discrete # noqa: F811 ): # Test evaluate tf.reset_default_graph() # Add a Curiosity module dummy_config.reward_signals = curiosity_dummy_config optimizer = create_sac_optimizer_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, optimizer.policy.brain) # Mock out reward signal eval update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"] update_buffer["curiosity_rewards"] = update_buffer["environment_rewards"] optimizer.update_reward_signals( {"curiosity": update_buffer}, num_sequences=update_buffer.num_experiences ) 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.hyperparameters.save_replay_buffer = True trainer = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, False, 0, 0) policy = trainer.create_policy(mock_brain.brain_name, 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.brain_name, mock_brain) trainer2.add_policy(mock_brain.brain_name, policy) assert trainer2.update_buffer.num_experiences == buffer_len @mock.patch("mlagents.trainers.sac.trainer.SACOptimizer") def test_add_get_policy(sac_optimizer, dummy_config): brain_params = make_brain_parameters( discrete_action=False, visual_inputs=0, vec_obs_size=6 ) mock_optimizer = mock.Mock() mock_optimizer.reward_signals = {} sac_optimizer.return_value = mock_optimizer trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0") policy = mock.Mock(spec=NNPolicy) 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_advance(dummy_config): brain_params = make_brain_parameters( discrete_action=False, visual_inputs=0, vec_obs_size=6 ) dummy_config.hyperparameters.steps_per_update = 20 dummy_config.hyperparameters.reward_signal_steps_per_update = 20 dummy_config.hyperparameters.buffer_init_steps = 0 trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0") policy = trainer.create_policy(brain_params.brain_name, brain_params) trainer.add_policy(brain_params.brain_name, policy) trajectory_queue = AgentManagerQueue("testbrain") policy_queue = AgentManagerQueue("testbrain") trainer.subscribe_trajectory_queue(trajectory_queue) trainer.publish_policy_queue(policy_queue) trajectory = make_fake_trajectory( length=15, max_step_complete=True, vec_obs_size=6, num_vis_obs=0, action_space=[2], is_discrete=False, ) 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=6, max_step_complete=False, vec_obs_size=6, num_vis_obs=0, action_space=[2], is_discrete=False, ) 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 # Assert we're not just using the default values assert ( trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").mean > 0 ) # Make sure there is a policy on the queue policy_queue.get_nowait() # Add another trajectory. Since this is less than 20 steps total (enough for) # two updates, there should NOT be a policy on the queue. trajectory = make_fake_trajectory( length=5, max_step_complete=False, vec_obs_size=6, num_vis_obs=0, action_space=[2], is_discrete=False, ) trajectory_queue.put(trajectory) trainer.advance() with pytest.raises(AgentManagerQueue.Empty): policy_queue.get_nowait() # Call add_policy and check that we update the correct number of times. # This is to emulate a load from checkpoint. policy = trainer.create_policy(brain_params.brain_name, brain_params) policy.get_current_step = lambda: 200 trainer.add_policy(brain_params.brain_name, policy) trainer.optimizer.update = mock.Mock() trainer.optimizer.update_reward_signals = mock.Mock() trainer.optimizer.update_reward_signals.return_value = {} trainer.optimizer.update.return_value = {} trajectory_queue.put(trajectory) trainer.advance() # Make sure we did exactly 1 update assert trainer.optimizer.update.call_count == 1 assert trainer.optimizer.update_reward_signals.call_count == 1 if __name__ == "__main__": pytest.main()