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 copy
import os
import mlagents.trainers.tests.mock_brain as mb
from mlagents.trainers.policy.tf_policy import TFPolicy
from mlagents.trainers.sac.optimizer import SACOptimizer
from mlagents.trainers.ppo.optimizer import PPOOptimizer
from mlagents.trainers.tests.test_simple_rl import PPO_CONFIG, SAC_CONFIG
from mlagents.trainers.settings import (
GAILSettings,
CuriositySettings,
RewardSignalSettings,
BehavioralCloningSettings,
NetworkSettings,
TrainerType,
RewardSignalType,
)
CONTINUOUS_PATH = os.path.dirname(os.path.abspath(__file__)) + "/test.demo"
DISCRETE_PATH = os.path.dirname(os.path.abspath(__file__)) + "/testdcvis.demo"
def ppo_dummy_config():
return copy.deepcopy(PPO_CONFIG)
def sac_dummy_config():
return copy.deepcopy(SAC_CONFIG)
@pytest.fixture
def gail_dummy_config():
return {RewardSignalType.GAIL: GAILSettings(demo_path=CONTINUOUS_PATH)}
@pytest.fixture
def curiosity_dummy_config():
return {RewardSignalType.CURIOSITY: CuriositySettings()}
@pytest.fixture
def extrinsic_dummy_config():
return {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
VECTOR_ACTION_SPACE = 2
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 20
BATCH_SIZE = 12
NUM_AGENTS = 12
def create_optimizer_mock(
trainer_config, reward_signal_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 if not use_visual else 0,
)
trainer_settings = trainer_config
trainer_settings.reward_signals = reward_signal_config
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
)
if trainer_settings.trainer_type == TrainerType.SAC:
optimizer = SACOptimizer(policy, trainer_settings)
else:
optimizer = PPOOptimizer(policy, trainer_settings)
return optimizer
def reward_signal_eval(optimizer, reward_signal_name):
buffer = mb.simulate_rollout(BATCH_SIZE, optimizer.policy.behavior_spec)
# Test evaluate
rsig_result = optimizer.reward_signals[reward_signal_name].evaluate_batch(buffer)
assert rsig_result.scaled_reward.shape == (BATCH_SIZE,)
assert rsig_result.unscaled_reward.shape == (BATCH_SIZE,)
def reward_signal_update(optimizer, reward_signal_name):
buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec)
feed_dict = optimizer.reward_signals[reward_signal_name].prepare_update(
optimizer.policy, buffer.make_mini_batch(0, 10), 2
)
out = optimizer.policy._execute_model(
feed_dict, optimizer.reward_signals[reward_signal_name].update_dict
)
assert type(out) is dict
@pytest.mark.parametrize(
"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
)
def test_gail_cc(trainer_config, gail_dummy_config):
trainer_config.behavioral_cloning = BehavioralCloningSettings(
demo_path=CONTINUOUS_PATH
)
optimizer = create_optimizer_mock(
trainer_config, gail_dummy_config, False, False, False
)
reward_signal_eval(optimizer, "gail")
reward_signal_update(optimizer, "gail")
@pytest.mark.parametrize(
"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
)
def test_gail_dc_visual(trainer_config, gail_dummy_config):
gail_dummy_config_discrete = {
RewardSignalType.GAIL: GAILSettings(demo_path=DISCRETE_PATH)
}
optimizer = create_optimizer_mock(
trainer_config, gail_dummy_config_discrete, False, True, True
)
reward_signal_eval(optimizer, "gail")
reward_signal_update(optimizer, "gail")
@pytest.mark.parametrize(
"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
)
def test_gail_rnn(trainer_config, gail_dummy_config):
policy = create_optimizer_mock(
trainer_config, gail_dummy_config, True, False, False
)
reward_signal_eval(policy, "gail")
reward_signal_update(policy, "gail")
@pytest.mark.parametrize(
"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
)
def test_curiosity_cc(trainer_config, curiosity_dummy_config):
policy = create_optimizer_mock(
trainer_config, curiosity_dummy_config, False, False, False
)
reward_signal_eval(policy, "curiosity")
reward_signal_update(policy, "curiosity")
@pytest.mark.parametrize(
"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
)
def test_curiosity_dc(trainer_config, curiosity_dummy_config):
policy = create_optimizer_mock(
trainer_config, curiosity_dummy_config, False, True, False
)
reward_signal_eval(policy, "curiosity")
reward_signal_update(policy, "curiosity")
@pytest.mark.parametrize(
"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
)
def test_curiosity_visual(trainer_config, curiosity_dummy_config):
policy = create_optimizer_mock(
trainer_config, curiosity_dummy_config, False, False, True
)
reward_signal_eval(policy, "curiosity")
reward_signal_update(policy, "curiosity")
@pytest.mark.parametrize(
"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
)
def test_curiosity_rnn(trainer_config, curiosity_dummy_config):
policy = create_optimizer_mock(
trainer_config, curiosity_dummy_config, True, False, False
)
reward_signal_eval(policy, "curiosity")
reward_signal_update(policy, "curiosity")
@pytest.mark.parametrize(
"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
)
def test_extrinsic(trainer_config, extrinsic_dummy_config):
policy = create_optimizer_mock(
trainer_config, extrinsic_dummy_config, False, False, False
)
reward_signal_eval(policy, "extrinsic")
reward_signal_update(policy, "extrinsic")
if __name__ == "__main__":
pytest.main()