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 os
import unittest
import tempfile
import numpy as np
from mlagents.model_serialization import SerializationSettings
from mlagents.tf_utils import tf
from mlagents.trainers.policy.tf_policy import TFPolicy
from mlagents.trainers.tf.models import ModelUtils, Tensor3DShape
from mlagents.trainers.exception import UnityTrainerException
from mlagents.trainers.tests import mock_brain as mb
from mlagents.trainers.settings import TrainerSettings, NetworkSettings, EncoderType
from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
from mlagents.trainers import __version__
VECTOR_ACTION_SPACE = 2
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 32
NUM_AGENTS = 12
def create_policy_mock(
dummy_config: TrainerSettings,
use_rnn: bool = False,
use_discrete: bool = True,
use_visual: bool = False,
model_path: str = "",
load: bool = False,
seed: int = 0,
) -> TFPolicy:
mock_spec = 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 = dummy_config
trainer_settings.keep_checkpoints = 3
trainer_settings.network_settings.memory = (
NetworkSettings.MemorySettings() if use_rnn else None
)
policy = TFPolicy(
seed, mock_spec, trainer_settings, model_path=model_path, load=load
)
return policy
def test_load_save(tmp_path):
path1 = os.path.join(tmp_path, "runid1")
path2 = os.path.join(tmp_path, "runid2")
trainer_params = TrainerSettings()
policy = create_policy_mock(trainer_params, model_path=path1)
policy.initialize_or_load()
policy._set_step(2000)
mock_brain_name = "MockBrain"
checkpoint_path = f"{policy.model_path}/{mock_brain_name}-2000"
serialization_settings = SerializationSettings(policy.model_path, mock_brain_name)
policy.checkpoint(checkpoint_path, serialization_settings)
assert len(os.listdir(tmp_path)) > 0
# Try load from this path
policy2 = create_policy_mock(trainer_params, model_path=path1, load=True, seed=1)
policy2.initialize_or_load()
_compare_two_policies(policy, policy2)
assert policy2.get_current_step() == 2000
# Try initialize from path 1
trainer_params.output_path = path2
trainer_params.init_path = path1
policy3 = create_policy_mock(trainer_params, model_path=path1, load=False, seed=2)
policy3.initialize_or_load()
_compare_two_policies(policy2, policy3)
# Assert that the steps are 0.
assert policy3.get_current_step() == 0
class ModelVersionTest(unittest.TestCase):
def test_version_compare(self):
# Test write_stats
with self.assertLogs("mlagents.trainers", level="WARNING") as cm:
path1 = tempfile.mkdtemp()
trainer_params = TrainerSettings()
policy = create_policy_mock(trainer_params, model_path=path1)
policy.initialize_or_load()
policy._check_model_version(
"0.0.0"
) # This is not the right version for sure
# Assert that 1 warning has been thrown with incorrect version
assert len(cm.output) == 1
policy._check_model_version(__version__) # This should be the right version
# Assert that no additional warnings have been thrown wth correct ver
assert len(cm.output) == 1
def _compare_two_policies(policy1: TFPolicy, policy2: TFPolicy) -> None:
"""
Make sure two policies have the same output for the same input.
"""
decision_step, _ = mb.create_steps_from_behavior_spec(
policy1.behavior_spec, num_agents=1
)
run_out1 = policy1.evaluate(decision_step, list(decision_step.agent_id))
run_out2 = policy2.evaluate(decision_step, list(decision_step.agent_id))
np.testing.assert_array_equal(run_out2["log_probs"], run_out1["log_probs"])
@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_policy_evaluate(rnn, visual, discrete):
# Test evaluate
tf.reset_default_graph()
policy = create_policy_mock(
TrainerSettings(), use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
decision_step, terminal_step = mb.create_steps_from_behavior_spec(
policy.behavior_spec, num_agents=NUM_AGENTS
)
run_out = policy.evaluate(decision_step, list(decision_step.agent_id))
if discrete:
run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE))
else:
assert run_out["action"].shape == (NUM_AGENTS, VECTOR_ACTION_SPACE)
def test_normalization():
behavior_spec = mb.setup_test_behavior_specs(
use_discrete=True, use_visual=False, vector_action_space=[2], vector_obs_space=1
)
time_horizon = 6
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1,)],
action_space=[2],
)
# Change half of the obs to 0
for i in range(3):
trajectory.steps[i].obs[0] = np.zeros(1, dtype=np.float32)
policy = TFPolicy(
0,
behavior_spec,
TrainerSettings(network_settings=NetworkSettings(normalize=True)),
"testdir",
False,
)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy.sess.run(
[policy.normalization_steps, policy.running_mean, policy.running_variance]
)
assert steps == 6
assert mean[0] == 0.5
# Note: variance is divided by number of steps, and initialized to 1 to avoid
# divide by 0. The right answer is 0.25
assert (variance[0] - 1) / steps == 0.25
# Make another update, this time with all 1's
time_horizon = 10
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1,)],
action_space=[2],
)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy.sess.run(
[policy.normalization_steps, policy.running_mean, policy.running_variance]
)
assert steps == 16
assert mean[0] == 0.8125
assert (variance[0] - 1) / steps == pytest.approx(0.152, abs=0.01)
def test_min_visual_size():
# Make sure each EncoderType has an entry in MIS_RESOLUTION_FOR_ENCODER
assert set(ModelUtils.MIN_RESOLUTION_FOR_ENCODER.keys()) == set(EncoderType)
for encoder_type in EncoderType:
with tf.Graph().as_default():
good_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type]
good_res = Tensor3DShape(width=good_size, height=good_size, num_channels=3)
vis_input = ModelUtils.create_visual_input(good_res, "test_min_visual_size")
ModelUtils._check_resolution_for_encoder(vis_input, encoder_type)
enc_func = ModelUtils.get_encoder_for_type(encoder_type)
enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False)
# Anything under the min size should raise an exception. If not, decrease the min size!
with pytest.raises(Exception):
with tf.Graph().as_default():
bad_size = ModelUtils.MIN_RESOLUTION_FOR_ENCODER[encoder_type] - 1
bad_res = Tensor3DShape(width=bad_size, height=bad_size, num_channels=3)
vis_input = ModelUtils.create_visual_input(
bad_res, "test_min_visual_size"
)
with pytest.raises(UnityTrainerException):
# Make sure we'd hit a friendly error during model setup time.
ModelUtils._check_resolution_for_encoder(vis_input, encoder_type)
enc_func = ModelUtils.get_encoder_for_type(encoder_type)
enc_func(vis_input, 32, ModelUtils.swish, 1, "test", False)
if __name__ == "__main__":
pytest.main()