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
from unittest import mock
import yaml
from mlagents.tf_utils import tf
from mlagents.trainers.sac.trainer import SACTrainer
from mlagents.trainers.sac.optimizer import SACOptimizer
from mlagents.trainers.common.nn_policy import NNPolicy
from mlagents.trainers.agent_processor import AgentManagerQueue
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
@pytest.fixture
def dummy_config():
return yaml.safe_load(
"""
trainer: sac
batch_size: 32
buffer_size: 10240
buffer_init_steps: 0
hidden_units: 32
init_entcoef: 0.1
learning_rate: 3.0e-4
max_steps: 1024
memory_size: 10
normalize: false
num_update: 1
train_interval: 1
num_layers: 1
time_horizon: 64
sequence_length: 16
summary_freq: 1000
tau: 0.005
use_recurrent: false
curiosity_enc_size: 128
demo_path: None
vis_encode_type: simple
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 = 64
NUM_AGENTS = 12
def create_sac_optimizer_mock(dummy_config, use_rnn, use_discrete, use_visual):
mock_brain = mb.setup_mock_brain(
use_discrete,
use_visual,
vector_action_space=VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
discrete_action_space=DISCRETE_ACTION_SPACE,
)
trainer_parameters = dummy_config
model_path = "testmodel"
trainer_parameters["model_path"] = model_path
trainer_parameters["keep_checkpoints"] = 3
trainer_parameters["use_recurrent"] = use_rnn
policy = NNPolicy(
0, mock_brain, trainer_parameters, False, False, create_tf_graph=False
)
optimizer = SACOptimizer(policy, trainer_parameters)
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_sac_optimizer_update(dummy_config, rnn, visual, discrete):
# Test evaluate
tf.reset_default_graph()
optimizer = create_sac_optimizer_mock(
dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
# Test update
update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, optimizer.policy.brain)
# Mock out reward signal eval
update_buffer["extrinsic_rewards"] = 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"])
def test_sac_update_reward_signals(dummy_config, discrete):
# Test evaluate
tf.reset_default_graph()
# Add a Curiosity module
dummy_config["reward_signals"]["curiosity"] = {}
dummy_config["reward_signals"]["curiosity"]["strength"] = 1.0
dummy_config["reward_signals"]["curiosity"]["gamma"] = 0.99
dummy_config["reward_signals"]["curiosity"]["encoding_size"] = 128
optimizer = create_sac_optimizer_mock(
dummy_config, use_rnn=False, use_discrete=discrete, use_visual=False
)
# Test update, while removing PPO-specific buffer elements.
update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, optimizer.policy.brain)
# Mock out reward signal eval
update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"]
update_buffer["curiosity_rewards"] = update_buffer["environment_rewards"]
optimizer.update_reward_signals(
{"curiosity": update_buffer}, num_sequences=update_buffer.num_experiences
)
def test_sac_save_load_buffer(tmpdir, dummy_config):
mock_brain = mb.setup_mock_brain(
False,
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["summary_path"] = str(tmpdir)
trainer_params["model_path"] = str(tmpdir)
trainer_params["save_replay_buffer"] = True
trainer = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, False, 0, 0)
policy = trainer.create_policy(mock_brain)
trainer.add_policy(mock_brain.brain_name, policy)
trainer.update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain)
buffer_len = trainer.update_buffer.num_experiences
trainer.save_model(mock_brain.brain_name)
# Wipe Trainer and try to load
trainer2 = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, True, 0, 0)
policy = trainer2.create_policy(mock_brain)
trainer2.add_policy(mock_brain.brain_name, policy)
assert trainer2.update_buffer.num_experiences == buffer_len
@mock.patch("mlagents.trainers.sac.trainer.SACOptimizer")
def test_add_get_policy(sac_optimizer, dummy_config):
brain_params = make_brain_parameters(
discrete_action=False, visual_inputs=0, vec_obs_size=6
)
mock_optimizer = mock.Mock()
mock_optimizer.reward_signals = {}
sac_optimizer.return_value = mock_optimizer
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
policy = mock.Mock(spec=NNPolicy)
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_process_trajectory(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 = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
policy = trainer.create_policy(brain_params)
trainer.add_policy(brain_params.brain_name, policy)
trajectory_queue = AgentManagerQueue("testbrain")
trainer.subscribe_trajectory_queue(trajectory_queue)
trajectory = make_fake_trajectory(
length=15,
max_step_complete=True,
vec_obs_size=6,
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 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=15,
max_step_complete=False,
vec_obs_size=6,
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
if __name__ == "__main__":
pytest.main()