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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import pytest
import numpy as np
from mlagents.tf_utils import tf
import attr
from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
from mlagents.trainers.policy.torch_policy import TorchPolicy
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
ppo_dummy_config,
curiosity_dummy_config,
gail_dummy_config,
)
@pytest.fixture
def dummy_config():
return attr.evolve(ppo_dummy_config(), framework=FrameworkType.PYTORCH)
VECTOR_ACTION_SPACE = 2
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 64
NUM_AGENTS = 12
def create_test_ppo_optimizer(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)
trainer_settings.network_settings.memory = (
NetworkSettings.MemorySettings(sequence_length=16, memory_size=10)
if use_rnn
else None
)
policy = TorchPolicy(0, mock_specs, trainer_settings, "test", False)
optimizer = TorchPPOOptimizer(policy, trainer_settings)
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_test_ppo_optimizer(
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,
memory_size=optimizer.policy.m_size,
)
# 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"]
# NOTE: In TensorFlow, the log_probs are saved as one for every discrete action, whereas
# in PyTorch it is saved as the total probability per branch. So we need to modify the
# log prob in the fake buffer here.
update_buffer["action_probs"] = np.ones_like(update_buffer["actions"])
return_stats = optimizer.update(
update_buffer,
num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
)
# Make sure we have the right stats
required_stats = [
"Losses/Policy Loss",
"Losses/Value Loss",
"Policy/Learning Rate",
"Policy/Epsilon",
"Policy/Beta",
]
for stat in required_stats:
assert stat in return_stats.keys()
@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_test_ppo_optimizer(
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,
memory_size=optimizer.policy.m_size,
)
# 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"]
# NOTE: In TensorFlow, the log_probs are saved as one for every discrete action, whereas
# in PyTorch it is saved as the total probability per branch. So we need to modify the
# log prob in the fake buffer here.
update_buffer["action_probs"] = np.ones_like(update_buffer["actions"])
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
dummy_config.reward_signals = gail_dummy_config
config = attr.evolve(ppo_dummy_config(), framework=FrameworkType.PYTORCH)
optimizer = create_test_ppo_optimizer(
config, 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"]
# NOTE: In TensorFlow, the log_probs are saved as one for every discrete action, whereas
# in PyTorch it is saved as the total probability per branch. So we need to modify the
# log prob in the fake buffer here.
update_buffer["action_probs"] = np.ones_like(update_buffer["actions"])
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):
optimizer = create_test_ppo_optimizer(
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
if __name__ == "__main__":
pytest.main()