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
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
VECTOR_ACTION_SPACE = 2
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 32
NUM_AGENTS = 12
EPSILON = 1e-7
def create_policy_mock(
dummy_config: TrainerSettings,
use_rnn: bool = False,
use_discrete: bool = True,
use_visual: 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)
return policy
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_large_normalization():
behavior_spec = mb.setup_test_behavior_specs(
use_discrete=True, use_visual=False, vector_action_space=[2], vector_obs_space=1
)
# Taken from Walker seed 3713 which causes NaN without proper initialization
large_obs1 = [
1800.00036621,
1799.96972656,
1800.01245117,
1800.07214355,
1800.02758789,
1799.98303223,
1799.88647461,
1799.89575195,
1800.03479004,
1800.14025879,
1800.17675781,
1800.20581055,
1800.33740234,
1800.36450195,
1800.43457031,
1800.45544434,
1800.44604492,
1800.56713867,
1800.73901367,
]
large_obs2 = [
1799.99975586,
1799.96679688,
1799.92980957,
1799.89550781,
1799.93774414,
1799.95300293,
1799.94067383,
1799.92993164,
1799.84057617,
1799.69873047,
1799.70605469,
1799.82849121,
1799.85095215,
1799.76977539,
1799.78283691,
1799.76708984,
1799.67163086,
1799.59191895,
1799.5135498,
1799.45556641,
1799.3717041,
]
policy = TFPolicy(
0,
behavior_spec,
TrainerSettings(network_settings=NetworkSettings(normalize=True)),
"testdir",
False,
)
time_horizon = len(large_obs1)
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1,)],
action_spec=behavior_spec.action_spec,
)
for i in range(time_horizon):
trajectory.steps[i].obs[0] = np.array([large_obs1[i]], dtype=np.float32)
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 mean[0] == pytest.approx(np.mean(large_obs1, dtype=np.float32), abs=0.01)
assert variance[0] / steps == pytest.approx(
np.var(large_obs1, dtype=np.float32), abs=0.01
)
time_horizon = len(large_obs2)
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1,)],
action_spec=behavior_spec.action_spec,
)
for i in range(time_horizon):
trajectory.steps[i].obs[0] = np.array([large_obs2[i]], dtype=np.float32)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
steps, mean, variance = policy.sess.run(
[policy.normalization_steps, policy.running_mean, policy.running_variance]
)
assert mean[0] == pytest.approx(
np.mean(large_obs1 + large_obs2, dtype=np.float32), abs=0.01
)
assert variance[0] / steps == pytest.approx(
np.var(large_obs1 + large_obs2, dtype=np.float32), abs=0.01
)
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_spec=behavior_spec.action_spec,
)
# 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 initalized to the variance of the initial trajectory + EPSILON
# (to avoid divide by 0) and multiplied by the number of steps. The correct answer is 0.25
assert variance[0] / steps == pytest.approx(0.25, abs=0.01)
# 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_spec=behavior_spec.action_spec,
)
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] / 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()