|
|
|
|
|
|
|
|
|
|
|
import yaml |
|
|
|
|
|
|
|
from mlagents.trainers.ppo.models import PPOModel |
|
|
|
from mlagents.trainers.ppo.trainer import PPOTrainer, discount_rewards |
|
|
|
from mlagents.trainers.common.nn_policy import NNPolicy |
|
|
|
from mlagents.trainers.models import EncoderType, LearningModel |
|
|
|
|
|
|
|
from mlagents_envs.environment import UnityEnvironment |
|
|
|
from mlagents_envs.mock_communicator import MockCommunicator |
|
|
|
from mlagents.trainers.brain_conversion_utils import group_spec_to_brain_parameters |
|
|
|
|
|
|
|
|
|
|
|
@pytest.fixture |
|
|
|
|
|
|
|
|
|
|
|
@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher") |
|
|
|
@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator") |
|
|
|
def test_ppo_policy_evaluate(mock_communicator, mock_launcher, dummy_config): |
|
|
|
tf.reset_default_graph() |
|
|
|
mock_communicator.return_value = MockCommunicator( |
|
|
|
discrete_action=False, visual_inputs=0 |
|
|
|
) |
|
|
|
env = UnityEnvironment(" ") |
|
|
|
env.reset() |
|
|
|
brain_name = env.get_agent_groups()[0] |
|
|
|
batched_step = env.get_step_result(brain_name) |
|
|
|
brain_params = group_spec_to_brain_parameters( |
|
|
|
brain_name, env.get_agent_group_spec(brain_name) |
|
|
|
) |
|
|
|
|
|
|
|
trainer_parameters = dummy_config |
|
|
|
model_path = brain_name |
|
|
|
trainer_parameters["model_path"] = model_path |
|
|
|
trainer_parameters["keep_checkpoints"] = 3 |
|
|
|
policy = NNPolicy(0, brain_params, trainer_parameters, False, False) |
|
|
|
run_out = policy.evaluate(batched_step, list(batched_step.agent_id)) |
|
|
|
assert run_out["action"].shape == (3, 2) |
|
|
|
env.close() |
|
|
|
|
|
|
|
|
|
|
|
@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher") |
|
|
|
@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator") |
|
|
|
def test_ppo_get_value_estimates(mock_communicator, mock_launcher, dummy_config): |
|
|
|
tf.reset_default_graph() |
|
|
|
|
|
|
|
|
|
|
|
batched_values = policy.get_batched_value_estimates(agentbuffer) |
|
|
|
for values in batched_values.values(): |
|
|
|
assert len(values) == 15 |
|
|
|
|
|
|
|
|
|
|
|
def test_ppo_model_cc_vector(): |
|
|
|
tf.reset_default_graph() |
|
|
|
with tf.Session() as sess: |
|
|
|
with tf.variable_scope("FakeGraphScope"): |
|
|
|
model = PPOModel( |
|
|
|
make_brain_parameters(discrete_action=False, visual_inputs=0) |
|
|
|
) |
|
|
|
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.epsilon: np.array([[0, 1], [2, 3]]), |
|
|
|
} |
|
|
|
sess.run(run_list, feed_dict=feed_dict) |
|
|
|
|
|
|
|
|
|
|
|
def test_ppo_model_cc_visual(): |
|
|
|
tf.reset_default_graph() |
|
|
|
with tf.Session() as sess: |
|
|
|
with tf.variable_scope("FakeGraphScope"): |
|
|
|
model = PPOModel( |
|
|
|
make_brain_parameters(discrete_action=False, visual_inputs=2) |
|
|
|
) |
|
|
|
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], dtype=np.float32), |
|
|
|
model.visual_in[1]: np.ones([2, 40, 30, 3], dtype=np.float32), |
|
|
|
model.epsilon: np.array([[0, 1], [2, 3]], dtype=np.float32), |
|
|
|
} |
|
|
|
sess.run(run_list, feed_dict=feed_dict) |
|
|
|
|
|
|
|
|
|
|
|
def test_ppo_model_dc_visual(): |
|
|
|
tf.reset_default_graph() |
|
|
|
with tf.Session() as sess: |
|
|
|
with tf.variable_scope("FakeGraphScope"): |
|
|
|
model = PPOModel( |
|
|
|
make_brain_parameters(discrete_action=True, visual_inputs=2) |
|
|
|
) |
|
|
|
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], 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_ppo_model_dc_vector(): |
|
|
|
tf.reset_default_graph() |
|
|
|
with tf.Session() as sess: |
|
|
|
with tf.variable_scope("FakeGraphScope"): |
|
|
|
model = PPOModel( |
|
|
|
make_brain_parameters(discrete_action=True, visual_inputs=0) |
|
|
|
) |
|
|
|
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], dtype=np.float32), |
|
|
|
} |
|
|
|
sess.run(run_list, feed_dict=feed_dict) |
|
|
|
|
|
|
|
|
|
|
|
def test_ppo_model_dc_vector_rnn(): |
|
|
|
tf.reset_default_graph() |
|
|
|
with tf.Session() as sess: |
|
|
|
with tf.variable_scope("FakeGraphScope"): |
|
|
|
memory_size = 128 |
|
|
|
model = PPOModel( |
|
|
|
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_ppo_model_cc_vector_rnn(): |
|
|
|
tf.reset_default_graph() |
|
|
|
with tf.Session() as sess: |
|
|
|
with tf.variable_scope("FakeGraphScope"): |
|
|
|
memory_size = 128 |
|
|
|
model = PPOModel( |
|
|
|
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]]), |
|
|
|
model.epsilon: np.array([[0, 1]]), |
|
|
|
} |
|
|
|
sess.run(run_list, feed_dict=feed_dict) |
|
|
|
|
|
|
|
|
|
|
|
def test_rl_functions(): |
|
|
|
|
Ervin Teng
4 年前