Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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from unittest import mock
import pytest
import numpy as np
from mlagents.tf_utils import tf
import attr
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
from mlagents.trainers.trainer.rl_trainer import RLTrainer
from mlagents.trainers.ppo.trainer import PPOTrainer, discount_rewards
from mlagents.trainers.ppo.optimizer_tf import PPOOptimizer
from mlagents.trainers.policy.tf_policy import TFPolicy
from mlagents.trainers.agent_processor import AgentManagerQueue
from mlagents.trainers.tests import mock_brain as mb
from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
from mlagents.trainers.settings import NetworkSettings, FrameworkType
from mlagents.trainers.tests.dummy_config import ( # noqa: F401; pylint: disable=unused-variable
curiosity_dummy_config,
gail_dummy_config,
ppo_dummy_config,
)
@pytest.fixture
def dummy_config():
return attr.evolve(ppo_dummy_config(), framework=FrameworkType.TENSORFLOW)
VECTOR_ACTION_SPACE = 2
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 64
NUM_AGENTS = 12
def _create_ppo_optimizer_ops_mock(dummy_config, use_rnn, use_discrete, use_visual):
mock_specs = mb.setup_test_behavior_specs(
use_discrete,
use_visual,
vector_action_space=DISCRETE_ACTION_SPACE
if use_discrete
else VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
)
trainer_settings = attr.evolve(dummy_config, framework=FrameworkType.TENSORFLOW)
trainer_settings.network_settings.memory = (
NetworkSettings.MemorySettings(sequence_length=16, memory_size=10)
if use_rnn
else None
)
policy = TFPolicy(
0, mock_specs, trainer_settings, "test", False, create_tf_graph=False
)
optimizer = PPOOptimizer(policy, trainer_settings)
policy.initialize()
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_ppo_optimizer_update(dummy_config, rnn, visual, discrete):
# Test evaluate
tf.reset_default_graph()
optimizer = _create_ppo_optimizer_ops_mock(
dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
# Test update
update_buffer = mb.simulate_rollout(
BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
)
# Mock out reward signal eval
update_buffer["advantages"] = update_buffer["environment_rewards"]
update_buffer["extrinsic_returns"] = update_buffer["environment_rewards"]
update_buffer["extrinsic_value_estimates"] = 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"])
@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
# We need to test this separately from test_reward_signals.py to ensure no interactions
def test_ppo_optimizer_update_curiosity(
dummy_config, curiosity_dummy_config, rnn, visual, discrete # noqa: F811
):
# Test evaluate
tf.reset_default_graph()
dummy_config.reward_signals = curiosity_dummy_config
optimizer = _create_ppo_optimizer_ops_mock(
dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
# Test update
update_buffer = mb.simulate_rollout(
BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
)
# Mock out reward signal eval
update_buffer["advantages"] = update_buffer["environment_rewards"]
update_buffer["extrinsic_returns"] = update_buffer["environment_rewards"]
update_buffer["extrinsic_value_estimates"] = update_buffer["environment_rewards"]
update_buffer["curiosity_returns"] = update_buffer["environment_rewards"]
update_buffer["curiosity_value_estimates"] = update_buffer["environment_rewards"]
optimizer.update(
update_buffer,
num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
)
# We need to test this separately from test_reward_signals.py to ensure no interactions
def test_ppo_optimizer_update_gail(gail_dummy_config, dummy_config): # noqa: F811
# Test evaluate
tf.reset_default_graph()
dummy_config.reward_signals = gail_dummy_config
optimizer = _create_ppo_optimizer_ops_mock(
attr.evolve(ppo_dummy_config(), framework=FrameworkType.TENSORFLOW),
use_rnn=False,
use_discrete=False,
use_visual=False,
)
# Test update
update_buffer = mb.simulate_rollout(
BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
)
# Mock out reward signal eval
update_buffer["advantages"] = update_buffer["environment_rewards"]
update_buffer["extrinsic_returns"] = update_buffer["environment_rewards"]
update_buffer["extrinsic_value_estimates"] = update_buffer["environment_rewards"]
update_buffer["gail_returns"] = update_buffer["environment_rewards"]
update_buffer["gail_value_estimates"] = update_buffer["environment_rewards"]
optimizer.update(
update_buffer,
num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
)
# Check if buffer size is too big
update_buffer = mb.simulate_rollout(3000, optimizer.policy.behavior_spec)
# Mock out reward signal eval
update_buffer["advantages"] = update_buffer["environment_rewards"]
update_buffer["extrinsic_returns"] = update_buffer["environment_rewards"]
update_buffer["extrinsic_value_estimates"] = update_buffer["environment_rewards"]
update_buffer["gail_returns"] = update_buffer["environment_rewards"]
update_buffer["gail_value_estimates"] = 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"])
@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
def test_ppo_get_value_estimates(dummy_config, rnn, visual, discrete):
tf.reset_default_graph()
optimizer = _create_ppo_optimizer_ops_mock(
dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
time_horizon = 15
trajectory = make_fake_trajectory(
length=time_horizon,
observation_shapes=optimizer.policy.behavior_spec.observation_shapes,
max_step_complete=True,
action_space=DISCRETE_ACTION_SPACE if discrete else VECTOR_ACTION_SPACE,
is_discrete=discrete,
)
run_out, final_value_out = optimizer.get_trajectory_value_estimates(
trajectory.to_agentbuffer(), trajectory.next_obs, done=False
)
for key, val in run_out.items():
assert type(key) is str
assert len(val) == 15
run_out, final_value_out = optimizer.get_trajectory_value_estimates(
trajectory.to_agentbuffer(), trajectory.next_obs, done=True
)
for key, val in final_value_out.items():
assert type(key) is str
assert val == 0.0
# Check if we ignore terminal states properly
optimizer.reward_signals["extrinsic"].use_terminal_states = False
run_out, final_value_out = optimizer.get_trajectory_value_estimates(
trajectory.to_agentbuffer(), trajectory.next_obs, done=False
)
for key, val in final_value_out.items():
assert type(key) is str
assert val != 0.0
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)
)
@mock.patch.object(RLTrainer, "create_model_saver")
@mock.patch("mlagents.trainers.ppo.trainer.PPOOptimizer")
def test_trainer_increment_step(ppo_optimizer, mock_create_model_saver):
trainer_params = attr.evolve(
attr.evolve(ppo_dummy_config(), framework=FrameworkType.TENSORFLOW),
framework=FrameworkType.TENSORFLOW,
)
mock_optimizer = mock.Mock()
mock_optimizer.reward_signals = {}
ppo_optimizer.return_value = mock_optimizer
trainer = PPOTrainer("test_brain", 0, trainer_params, True, False, 0, "0")
policy_mock = mock.Mock(spec=TFPolicy)
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)
behavior_id = BehaviorIdentifiers.from_name_behavior_id(trainer.brain_name)
trainer.add_policy(behavior_id, policy_mock)
trainer._increment_step(5, trainer.brain_name)
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, curiosity_dummy_config, use_discrete # noqa: F811
):
mock_behavior_spec = mb.setup_test_behavior_specs(
use_discrete,
False,
vector_action_space=DISCRETE_ACTION_SPACE
if use_discrete
else VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
)
trainer_params = dummy_config
trainer_params.network_settings.memory = NetworkSettings.MemorySettings(
memory_size=10, sequence_length=16
)
# Test curiosity reward signal
trainer_params.reward_signals = curiosity_dummy_config
mock_brain_name = "MockBrain"
behavior_id = BehaviorIdentifiers.from_name_behavior_id(mock_brain_name)
trainer = PPOTrainer("test", 0, trainer_params, True, False, 0, "0")
policy = trainer.create_policy(behavior_id, mock_behavior_spec)
trainer.add_policy(behavior_id, policy)
# Test update with sequence length smaller than batch size
buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, mock_behavior_spec)
# 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()
def test_process_trajectory(dummy_config):
behavior_spec = mb.setup_test_behavior_specs(
True,
False,
vector_action_space=DISCRETE_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
)
mock_brain_name = "MockBrain"
behavior_id = BehaviorIdentifiers.from_name_behavior_id(mock_brain_name)
trainer = PPOTrainer("test_brain", 0, dummy_config, True, False, 0, "0")
policy = trainer.create_policy(behavior_id, behavior_spec)
trainer.add_policy(behavior_id, policy)
trajectory_queue = AgentManagerQueue("testbrain")
trainer.subscribe_trajectory_queue(trajectory_queue)
time_horizon = 15
trajectory = make_fake_trajectory(
length=time_horizon,
observation_shapes=behavior_spec.observation_shapes,
max_step_complete=True,
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,
observation_shapes=behavior_spec.observation_shapes,
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
@mock.patch.object(RLTrainer, "create_model_saver")
@mock.patch("mlagents.trainers.ppo.trainer.PPOOptimizer")
def test_add_get_policy(ppo_optimizer, mock_create_model_saver, dummy_config):
mock_optimizer = mock.Mock()
mock_optimizer.reward_signals = {}
ppo_optimizer.return_value = mock_optimizer
trainer = PPOTrainer("test_policy", 0, dummy_config, True, False, 0, "0")
policy = mock.Mock(spec=TFPolicy)
policy.get_current_step.return_value = 2000
behavior_id = BehaviorIdentifiers.from_name_behavior_id(trainer.brain_name)
trainer.add_policy(behavior_id, policy)
assert trainer.get_policy("test_policy") == policy
# Make sure the summary steps were loaded properly
assert trainer.get_step == 2000
if __name__ == "__main__":
pytest.main()