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 copy
from mlagents.tf_utils import tf
from mlagents.trainers.sac.trainer import SACTrainer
from mlagents.trainers.sac.optimizer import SACOptimizer
from mlagents.trainers.policy.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
from mlagents.trainers.tests.test_simple_rl import SAC_CONFIG
from mlagents.trainers.settings import NetworkSettings
from mlagents.trainers.tests.test_reward_signals import ( # noqa: F401; pylint: disable=unused-variable
curiosity_dummy_config,
)
@pytest.fixture
def dummy_config():
return copy.deepcopy(SAC_CONFIG)
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_settings = dummy_config
trainer_settings.network_settings.memory = (
NetworkSettings.MemorySettings(sequence_length=16, memory_size=10)
if use_rnn
else None
)
policy = NNPolicy(
0, mock_brain, trainer_settings, False, "test", False, create_tf_graph=False
)
optimizer = SACOptimizer(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_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, curiosity_dummy_config, discrete # noqa: F811
):
# Test evaluate
tf.reset_default_graph()
# Add a Curiosity module
dummy_config.reward_signals = curiosity_dummy_config
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.hyperparameters.save_replay_buffer = True
trainer = SACTrainer(
mock_brain.brain_name, 1, trainer_params, True, False, 0, "testdir"
)
policy = trainer.create_policy(mock_brain.brain_name, 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, "testdir"
)
policy = trainer2.create_policy(mock_brain.brain_name, 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
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
# Test incorrect class of policy
policy = mock.Mock()
with pytest.raises(RuntimeError):
trainer.add_policy(brain_params, policy)
def test_advance(dummy_config):
brain_params = make_brain_parameters(
discrete_action=False, visual_inputs=0, vec_obs_size=6
)
dummy_config.hyperparameters.steps_per_update = 20
dummy_config.hyperparameters.reward_signal_steps_per_update = 20
dummy_config.hyperparameters.buffer_init_steps = 0
trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
policy = trainer.create_policy(brain_params.brain_name, brain_params)
trainer.add_policy(brain_params.brain_name, policy)
trajectory_queue = AgentManagerQueue("testbrain")
policy_queue = AgentManagerQueue("testbrain")
trainer.subscribe_trajectory_queue(trajectory_queue)
trainer.publish_policy_queue(policy_queue)
trajectory = make_fake_trajectory(
length=15,
max_step_complete=True,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
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=6,
max_step_complete=False,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
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
# Assert we're not just using the default values
assert (
trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").mean > 0
)
# Make sure there is a policy on the queue
policy_queue.get_nowait()
# Add another trajectory. Since this is less than 20 steps total (enough for)
# two updates, there should NOT be a policy on the queue.
trajectory = make_fake_trajectory(
length=5,
max_step_complete=False,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
trajectory_queue.put(trajectory)
trainer.advance()
with pytest.raises(AgentManagerQueue.Empty):
policy_queue.get_nowait()
# Call add_policy and check that we update the correct number of times.
# This is to emulate a load from checkpoint.
policy = trainer.create_policy(brain_params.brain_name, brain_params)
policy.get_current_step = lambda: 200
trainer.add_policy(brain_params.brain_name, policy)
trainer.optimizer.update = mock.Mock()
trainer.optimizer.update_reward_signals = mock.Mock()
trainer.optimizer.update_reward_signals.return_value = {}
trainer.optimizer.update.return_value = {}
trajectory_queue.put(trajectory)
trainer.advance()
# Make sure we did exactly 1 update
assert trainer.optimizer.update.call_count == 1
assert trainer.optimizer.update_reward_signals.call_count == 1
if __name__ == "__main__":
pytest.main()