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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from unittest import mock
import pytest
import numpy as np
from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.base_env import DecisionSteps, TerminalSteps
from mlagents_envs.exception import UnityEnvironmentException, UnityActionException
from mlagents_envs.mock_communicator import MockCommunicator
@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
def test_handles_bad_filename(get_communicator):
with pytest.raises(UnityEnvironmentException):
UnityEnvironment(" ")
@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
def test_initialization(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0
)
env = UnityEnvironment(" ")
assert env.get_behavior_names() == ["RealFakeBrain"]
env.close()
@pytest.mark.parametrize(
"base_port,file_name,expected",
[
# Non-None base port value will always be used
(6001, "foo.exe", 6001),
# No port specified and environment specified, so use BASE_ENVIRONMENT_PORT
(None, "foo.exe", UnityEnvironment.BASE_ENVIRONMENT_PORT),
# No port specified and no environment, so use DEFAULT_EDITOR_PORT
(None, None, UnityEnvironment.DEFAULT_EDITOR_PORT),
],
)
@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
def test_port_defaults(
mock_communicator, mock_launcher, base_port, file_name, expected
):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0
)
env = UnityEnvironment(file_name=file_name, worker_id=0, base_port=base_port)
assert expected == env.port
@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
def test_reset(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0
)
env = UnityEnvironment(" ")
spec = env.get_behavior_spec("RealFakeBrain")
env.reset()
decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
env.close()
assert isinstance(decision_steps, DecisionSteps)
assert isinstance(terminal_steps, TerminalSteps)
assert len(spec.observation_shapes) == len(decision_steps.obs)
assert len(spec.observation_shapes) == len(terminal_steps.obs)
n_agents = len(decision_steps)
for shape, obs in zip(spec.observation_shapes, decision_steps.obs):
assert (n_agents,) + shape == obs.shape
n_agents = len(terminal_steps)
for shape, obs in zip(spec.observation_shapes, terminal_steps.obs):
assert (n_agents,) + shape == obs.shape
@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
def test_step(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0
)
env = UnityEnvironment(" ")
spec = env.get_behavior_spec("RealFakeBrain")
env.step()
decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
n_agents = len(decision_steps)
env.set_actions(
"RealFakeBrain", np.zeros((n_agents, spec.action_size), dtype=np.float32)
)
env.step()
with pytest.raises(UnityActionException):
env.set_actions(
"RealFakeBrain",
np.zeros((n_agents - 1, spec.action_size), dtype=np.float32),
)
decision_steps, terminal_steps = env.get_steps("RealFakeBrain")
n_agents = len(decision_steps)
env.set_actions(
"RealFakeBrain", -1 * np.ones((n_agents, spec.action_size), dtype=np.float32)
)
env.step()
env.close()
assert isinstance(decision_steps, DecisionSteps)
assert isinstance(terminal_steps, TerminalSteps)
assert len(spec.observation_shapes) == len(decision_steps.obs)
assert len(spec.observation_shapes) == len(terminal_steps.obs)
for shape, obs in zip(spec.observation_shapes, decision_steps.obs):
assert (n_agents,) + shape == obs.shape
assert 0 in decision_steps
assert 2 in terminal_steps
@mock.patch("mlagents_envs.environment.UnityEnvironment.executable_launcher")
@mock.patch("mlagents_envs.environment.UnityEnvironment.get_communicator")
def test_close(mock_communicator, mock_launcher):
comm = MockCommunicator(discrete_action=False, visual_inputs=0)
mock_communicator.return_value = comm
env = UnityEnvironment(" ")
assert env._loaded
env.close()
assert not env._loaded
assert comm.has_been_closed
def test_check_communication_compatibility():
unity_ver = "1.0.0"
python_ver = "1.0.0"
unity_package_version = "0.15.0"
assert UnityEnvironment.check_communication_compatibility(
unity_ver, python_ver, unity_package_version
)
unity_ver = "1.1.0"
assert UnityEnvironment.check_communication_compatibility(
unity_ver, python_ver, unity_package_version
)
unity_ver = "2.0.0"
assert not UnityEnvironment.check_communication_compatibility(
unity_ver, python_ver, unity_package_version
)
unity_ver = "0.16.0"
python_ver = "0.16.0"
assert UnityEnvironment.check_communication_compatibility(
unity_ver, python_ver, unity_package_version
)
unity_ver = "0.17.0"
assert not UnityEnvironment.check_communication_compatibility(
unity_ver, python_ver, unity_package_version
)
unity_ver = "1.16.0"
assert not UnityEnvironment.check_communication_compatibility(
unity_ver, python_ver, unity_package_version
)
def test_returncode_to_signal_name():
assert UnityEnvironment.returncode_to_signal_name(-2) == "SIGINT"
assert UnityEnvironment.returncode_to_signal_name(42) is None
assert UnityEnvironment.returncode_to_signal_name("SIGINT") is None
if __name__ == "__main__":
pytest.main()