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 unittest.mock as mock
import pytest
import numpy as np
from gym import spaces
from gym_unity.envs import UnityEnv, UnityGymException
from mlagents.envs.brain import CameraResolution
@mock.patch("gym_unity.envs.UnityEnvironment")
def test_gym_wrapper(mock_env):
mock_brain = create_mock_brainparams()
mock_braininfo = create_mock_vector_braininfo()
setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
env = UnityEnv(" ", use_visual=False, multiagent=False)
assert isinstance(env, UnityEnv)
assert isinstance(env.reset(), np.ndarray)
actions = env.action_space.sample()
assert actions.shape[0] == 2
obs, rew, done, info = env.step(actions)
assert env.observation_space.contains(obs)
assert isinstance(obs, np.ndarray)
assert isinstance(rew, float)
assert isinstance(done, bool)
assert isinstance(info, dict)
@mock.patch("gym_unity.envs.UnityEnvironment")
def test_multi_agent(mock_env):
mock_brain = create_mock_brainparams()
mock_braininfo = create_mock_vector_braininfo(num_agents=2)
setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
with pytest.raises(UnityGymException):
UnityEnv(" ", multiagent=False)
env = UnityEnv(" ", use_visual=False, multiagent=True)
assert isinstance(env.reset(), list)
actions = [env.action_space.sample() for i in range(env.number_agents)]
obs, rew, done, info = env.step(actions)
assert isinstance(obs, list)
assert isinstance(rew, list)
assert isinstance(done, list)
assert isinstance(info, dict)
@mock.patch("gym_unity.envs.UnityEnvironment")
def test_branched_flatten(mock_env):
mock_brain = create_mock_brainparams(
vector_action_space_type="discrete", vector_action_space_size=[2, 2, 3]
)
mock_braininfo = create_mock_vector_braininfo(num_agents=1)
setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
env = UnityEnv(" ", use_visual=False, multiagent=False, flatten_branched=True)
assert isinstance(env.action_space, spaces.Discrete)
assert env.action_space.n == 12
assert env._flattener.lookup_action(0) == [0, 0, 0]
assert env._flattener.lookup_action(11) == [1, 1, 2]
# Check that False produces a MultiDiscrete
env = UnityEnv(" ", use_visual=False, multiagent=False, flatten_branched=False)
assert isinstance(env.action_space, spaces.MultiDiscrete)
@pytest.mark.parametrize("use_uint8", [True, False], ids=["float", "uint8"])
@mock.patch("gym_unity.envs.UnityEnvironment")
def test_gym_wrapper_visual(mock_env, use_uint8):
mock_brain = create_mock_brainparams(number_visual_observations=1)
mock_braininfo = create_mock_vector_braininfo(number_visual_observations=1)
setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
env = UnityEnv(" ", use_visual=True, multiagent=False, uint8_visual=use_uint8)
assert isinstance(env, UnityEnv)
assert isinstance(env.reset(), np.ndarray)
actions = env.action_space.sample()
assert actions.shape[0] == 2
obs, rew, done, info = env.step(actions)
assert env.observation_space.contains(obs)
assert isinstance(obs, np.ndarray)
assert isinstance(rew, float)
assert isinstance(done, bool)
assert isinstance(info, dict)
# Helper methods
def create_mock_brainparams(
number_visual_observations=0,
num_stacked_vector_observations=1,
vector_action_space_type="continuous",
vector_observation_space_size=3,
vector_action_space_size=None,
):
"""
Creates a mock BrainParameters object with parameters.
"""
# Avoid using mutable object as default param
if vector_action_space_size is None:
vector_action_space_size = [2]
mock_brain = mock.Mock()
mock_brain.return_value.number_visual_observations = number_visual_observations
if number_visual_observations:
mock_brain.return_value.camera_resolutions = [
CameraResolution(width=8, height=8, num_channels=3)
for _ in range(number_visual_observations)
]
mock_brain.return_value.num_stacked_vector_observations = (
num_stacked_vector_observations
)
mock_brain.return_value.vector_action_space_type = vector_action_space_type
mock_brain.return_value.vector_observation_space_size = (
vector_observation_space_size
)
mock_brain.return_value.vector_action_space_size = vector_action_space_size
return mock_brain()
def create_mock_vector_braininfo(num_agents=1, number_visual_observations=0):
"""
Creates a mock BrainInfo with vector observations. Imitates constant
vector observations, rewards, dones, and agents.
:int num_agents: Number of "agents" to imitate in your BrainInfo values.
"""
mock_braininfo = mock.Mock()
mock_braininfo.return_value.vector_observations = np.array([num_agents * [1, 2, 3]])
if number_visual_observations:
mock_braininfo.return_value.visual_observations = [[np.zeros(shape=(8, 8, 3))]]
mock_braininfo.return_value.rewards = num_agents * [1.0]
mock_braininfo.return_value.local_done = num_agents * [False]
mock_braininfo.return_value.text_observations = num_agents * [""]
mock_braininfo.return_value.agents = range(0, num_agents)
return mock_braininfo()
def setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo):
"""
Takes a mock UnityEnvironment and adds the appropriate properties, defined by the mock
BrainParameters and BrainInfo.
:Mock mock_env: A mock UnityEnvironment, usually empty.
:Mock mock_brain: A mock Brain object that specifies the params of this environment.
:Mock mock_braininfo: A mock BrainInfo object that will be returned at each step and reset.
"""
mock_env.return_value.academy_name = "MockAcademy"
mock_env.return_value.brains = {"MockBrain": mock_brain}
mock_env.return_value.external_brain_names = ["MockBrain"]
mock_env.return_value.reset.return_value = {"MockBrain": mock_braininfo}
mock_env.return_value.step.return_value = {"MockBrain": mock_braininfo}