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from unittest import mock
import pytest
import numpy as np
from mlagents.tf_utils import tf
import yaml
from mlagents.trainers.ppo.models import PPOModel
from mlagents.trainers.ppo.trainer import PPOTrainer, discount_rewards
from mlagents.trainers.ppo.policy import PPOPolicy
from mlagents.trainers.models import EncoderType, LearningModel
from mlagents.trainers.trainer import UnityTrainerException
from mlagents.trainers.brain import BrainParameters, CameraResolution
from mlagents.trainers.agent_processor import AgentManagerQueue
from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.mock_communicator import MockCommunicator
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.brain_conversion_utils import group_spec_to_brain_parameters
@pytest.fixture
def dummy_config():
return yaml.safe_load(
"""
trainer: ppo
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
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
normalize: true
memory_size: 8
curiosity_strength: 0.0
curiosity_enc_size: 1
summary_path: test
model_path: test
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
@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 = PPOPolicy(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()
brain_params = BrainParameters(
brain_name="test_brain",
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
policy = PPOPolicy(0, brain_params, dummy_config, False, False)
time_horizon = 15
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
vec_obs_size=1,
num_vis_obs=0,
action_space=[2],
)
run_out = policy.get_value_estimates(trajectory.next_obs, "test_agent", done=False)
for key, val in run_out.items():
assert type(key) is str
assert type(val) is float
run_out = policy.get_value_estimates(trajectory.next_obs, "test_agent", done=True)
for key, val in run_out.items():
assert type(key) is str
assert val == 0.0
# Check if we ignore terminal states properly
policy.reward_signals["extrinsic"].use_terminal_states = False
run_out = policy.get_value_estimates(trajectory.next_obs, "test_agent", done=True)
for key, val in run_out.items():
assert type(key) is str
assert val != 0.0
agentbuffer = trajectory.to_agentbuffer()
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():
rewards = np.array([0.0, 0.0, 0.0, 1.0], dtype=np.float32)
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], dtype=np.float32)
)
def test_trainer_increment_step(dummy_config):
trainer_params = dummy_config
brain_params = BrainParameters(
brain_name="test_brain",
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
trainer = PPOTrainer(
brain_params.brain_name, 0, trainer_params, True, False, 0, "0", False
)
policy_mock = mock.Mock(spec=PPOPolicy)
policy_mock.get_current_step.return_value = 0
step_count = (
5
) # 10 hacked because this function is no longer called through trainer
policy_mock.increment_step = mock.Mock(return_value=step_count)
trainer.add_policy("testbehavior", policy_mock)
trainer._increment_step(5, "testbehavior")
policy_mock.increment_step.assert_called_with(5)
assert trainer.step == step_count
@pytest.mark.parametrize("use_discrete", [True, False])
def test_trainer_update_policy(dummy_config, use_discrete):
mock_brain = mb.setup_mock_brain(
use_discrete,
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["use_recurrent"] = True
# Test curiosity reward signal
trainer_params["reward_signals"]["curiosity"] = {}
trainer_params["reward_signals"]["curiosity"]["strength"] = 1.0
trainer_params["reward_signals"]["curiosity"]["gamma"] = 0.99
trainer_params["reward_signals"]["curiosity"]["encoding_size"] = 128
trainer = PPOTrainer(
mock_brain.brain_name, 0, trainer_params, True, False, 0, "0", False
)
policy = trainer.create_policy(mock_brain)
trainer.add_policy(mock_brain.brain_name, policy)
# Test update with sequence length smaller than batch size
buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, mock_brain)
# Mock out reward signal eval
buffer["extrinsic_rewards"] = buffer["environment_rewards"]
buffer["extrinsic_returns"] = buffer["environment_rewards"]
buffer["extrinsic_value_estimates"] = buffer["environment_rewards"]
buffer["curiosity_rewards"] = buffer["environment_rewards"]
buffer["curiosity_returns"] = buffer["environment_rewards"]
buffer["curiosity_value_estimates"] = buffer["environment_rewards"]
buffer["advantages"] = buffer["environment_rewards"]
trainer.update_buffer = buffer
trainer._update_policy()
# Make batch length a larger multiple of sequence length
trainer.trainer_parameters["batch_size"] = 128
trainer._update_policy()
# Make batch length a larger non-multiple of sequence length
trainer.trainer_parameters["batch_size"] = 100
trainer._update_policy()
def test_process_trajectory(dummy_config):
brain_params = BrainParameters(
brain_name="test_brain",
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
trainer = PPOTrainer(brain_params, 0, dummy_config, True, False, 0, "0", False)
policy = trainer.create_policy(brain_params)
trainer.add_policy(brain_params.brain_name, policy)
trajectory_queue = AgentManagerQueue("testbrain")
trainer.subscribe_trajectory_queue(trajectory_queue)
time_horizon = 15
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
vec_obs_size=1,
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 GAE worked
assert (
"advantages" in trainer.update_buffer
and "discounted_returns" in trainer.update_buffer
)
# 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=time_horizon + 1,
max_step_complete=False,
vec_obs_size=1,
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
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 = PPOTrainer(brain_params, 0, dummy_config, True, False, 0, "0", False)
policy = mock.Mock(spec=PPOPolicy)
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_normalization(dummy_config):
brain_params = BrainParameters(
brain_name="test_brain",
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
trainer = PPOTrainer(
brain_params.brain_name, 0, dummy_config, True, False, 0, "0", False
)
time_horizon = 6
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
vec_obs_size=1,
num_vis_obs=0,
action_space=[2],
)
# Change half of the obs to 0
for i in range(3):
trajectory.steps[i].obs[0] = np.zeros(1, dtype=np.float32)
policy = trainer.create_policy(brain_params)
trainer.add_policy(brain_params.brain_name, policy)
trainer._process_trajectory(trajectory)
# Check that the running mean and variance is correct
steps, mean, variance = trainer.policy.sess.run(
[
trainer.policy.model.normalization_steps,
trainer.policy.model.running_mean,
trainer.policy.model.running_variance,
]
)
assert steps == 6
assert mean[0] == 0.5
# Note: variance is divided by number of steps, and initialized to 1 to avoid
# divide by 0. The right answer is 0.25
assert (variance[0] - 1) / steps == 0.25
# Make another update, this time with all 1's
time_horizon = 10
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
vec_obs_size=1,
num_vis_obs=0,
action_space=[2],
)
trainer._process_trajectory(trajectory)
# Check that the running mean and variance is correct
steps, mean, variance = trainer.policy.sess.run(
[
trainer.policy.model.normalization_steps,
trainer.policy.model.running_mean,
trainer.policy.model.running_variance,
]
)
assert steps == 16
assert mean[0] == 0.8125
assert (variance[0] - 1) / steps == pytest.approx(0.152, abs=0.01)
def test_min_visual_size():
# Make sure each EncoderType has an entry in MIS_RESOLUTION_FOR_ENCODER
assert set(LearningModel.MIN_RESOLUTION_FOR_ENCODER.keys()) == set(EncoderType)
for encoder_type in EncoderType:
with tf.Graph().as_default():
good_size = LearningModel.MIN_RESOLUTION_FOR_ENCODER[encoder_type]
good_res = CameraResolution(
width=good_size, height=good_size, num_channels=3
)
LearningModel._check_resolution_for_encoder(good_res, encoder_type)
vis_input = LearningModel.create_visual_input(
good_res, "test_min_visual_size"
)
enc_func = LearningModel.get_encoder_for_type(encoder_type)
enc_func(vis_input, 32, LearningModel.swish, 1, "test", False)
# Anything under the min size should raise an exception. If not, decrease the min size!
with pytest.raises(Exception):
with tf.Graph().as_default():
bad_size = LearningModel.MIN_RESOLUTION_FOR_ENCODER[encoder_type] - 1
bad_res = CameraResolution(
width=bad_size, height=bad_size, num_channels=3
)
with pytest.raises(UnityTrainerException):
# Make sure we'd hit a friendly error during model setup time.
LearningModel._check_resolution_for_encoder(bad_res, encoder_type)
vis_input = LearningModel.create_visual_input(
bad_res, "test_min_visual_size"
)
enc_func = LearningModel.get_encoder_for_type(encoder_type)
enc_func(vis_input, 32, LearningModel.swish, 1, "test", False)
if __name__ == "__main__":
pytest.main()