import unittest.mock as mock import pytest import numpy as np import tensorflow as tf import yaml from mlagents.trainers.ppo.models import PPOModel from mlagents.trainers.ppo.trainer import discount_rewards from mlagents.trainers.ppo.policy import PPOPolicy from mlagents.envs import UnityEnvironment from tests.mock_communicator import MockCommunicator @pytest.fixture def dummy_config(): return yaml.load( ''' 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 ''') @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_policy_evaluate(mock_communicator, mock_launcher): tf.reset_default_graph() mock_communicator.return_value = MockCommunicator( discrete_action=False, visual_inputs=0) env = UnityEnvironment(' ') brain_infos = env.reset() brain_info = brain_infos[env.brain_names[0]] trainer_parameters = dummy_config() model_path = env.brain_names[0] trainer_parameters['model_path'] = model_path trainer_parameters['keep_checkpoints'] = 3 policy = PPOPolicy(0, env.brains[env.brain_names[0]], trainer_parameters, False, False) run_out = policy.evaluate(brain_info) assert run_out['action'].shape == (3, 2) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_cc_vector(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=False, visual_inputs=0) env = UnityEnvironment(' ') model = PPOModel(env.brains["RealFakeBrain"]) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.log_probs, 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) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_cc_visual(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=False, visual_inputs=2) env = UnityEnvironment(' ') model = PPOModel(env.brains["RealFakeBrain"]) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.log_probs, 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]), model.visual_in[1]: np.ones([2, 40, 30, 3])} sess.run(run_list, feed_dict=feed_dict) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_dc_visual(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=True, visual_inputs=2) env = UnityEnvironment(' ') model = PPOModel(env.brains["RealFakeBrain"]) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.all_log_probs, 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]), model.visual_in[1]: np.ones([2, 40, 30, 3]), model.action_masks: np.ones([2,2]) } sess.run(run_list, feed_dict=feed_dict) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_dc_vector(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=True, visual_inputs=0) env = UnityEnvironment(' ') model = PPOModel(env.brains["RealFakeBrain"]) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.all_log_probs, 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])} sess.run(run_list, feed_dict=feed_dict) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_dc_vector_rnn(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=True, visual_inputs=0) env = UnityEnvironment(' ') memory_size = 128 model = PPOModel(env.brains["RealFakeBrain"], 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)), model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.action_masks: np.ones([1,2])} sess.run(run_list, feed_dict=feed_dict) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_cc_vector_rnn(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=False, visual_inputs=0) env = UnityEnvironment(' ') memory_size = 128 model = PPOModel(env.brains["RealFakeBrain"], 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)), 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) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_dc_vector_curio(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=True, visual_inputs=0) env = UnityEnvironment(' ') model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.all_log_probs, model.value, model.entropy, model.learning_rate, model.intrinsic_reward] 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.next_vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.action_holder: [[0], [0]], model.action_masks: np.ones([2,2])} sess.run(run_list, feed_dict=feed_dict) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_cc_vector_curio(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=False, visual_inputs=0) env = UnityEnvironment(' ') model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.all_log_probs, model.value, model.entropy, model.learning_rate, model.intrinsic_reward] 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.next_vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.output: [[0.0, 0.0], [0.0, 0.0]]} sess.run(run_list, feed_dict=feed_dict) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_dc_visual_curio(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=True, visual_inputs=2) env = UnityEnvironment(' ') model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.all_log_probs, model.value, model.entropy, model.learning_rate, model.intrinsic_reward] 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.next_vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.action_holder: [[0], [0]], model.visual_in[0]: np.ones([2, 40, 30, 3]), model.visual_in[1]: np.ones([2, 40, 30, 3]), model.next_visual_in[0]: np.ones([2, 40, 30, 3]), model.next_visual_in[1]: np.ones([2, 40, 30, 3]), model.action_masks: np.ones([2,2]) } sess.run(run_list, feed_dict=feed_dict) env.close() @mock.patch('mlagents.envs.UnityEnvironment.executable_launcher') @mock.patch('mlagents.envs.UnityEnvironment.get_communicator') def test_ppo_model_cc_visual_curio(mock_communicator, mock_launcher): tf.reset_default_graph() with tf.Session() as sess: with tf.variable_scope("FakeGraphScope"): mock_communicator.return_value = MockCommunicator( discrete_action=False, visual_inputs=2) env = UnityEnvironment(' ') model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True) init = tf.global_variables_initializer() sess.run(init) run_list = [model.output, model.all_log_probs, model.value, model.entropy, model.learning_rate, model.intrinsic_reward] 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.next_vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]), model.output: [[0.0, 0.0], [0.0, 0.0]], model.visual_in[0]: np.ones([2, 40, 30, 3]), model.visual_in[1]: np.ones([2, 40, 30, 3]), model.next_visual_in[0]: np.ones([2, 40, 30, 3]), model.next_visual_in[1]: np.ones([2, 40, 30, 3]) } sess.run(run_list, feed_dict=feed_dict) env.close() def test_rl_functions(): rewards = np.array([0.0, 0.0, 0.0, 1.0]) gamma = 0.9 returns = discount_rewards(rewards, gamma, 0.0) np.testing.assert_array_almost_equal(returns, np.array([0.729, 0.81, 0.9, 1.0])) if __name__ == '__main__': pytest.main()