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340 行
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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()