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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46 行
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import unittest.mock as mock
import pytest
import numpy as np
from gym_unity.envs import UnityEnv, UnityGymException
from tests.mock_communicator import MockCommunicator
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_gym_wrapper(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0, stack=False, num_agents=1)
# Test for incorrect number of agents.
with pytest.raises(UnityGymException):
UnityEnv(' ', use_visual=False, multiagent=True)
env = UnityEnv(' ', use_visual=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 isinstance(obs, np.ndarray)
assert isinstance(rew, float)
assert isinstance(done, bool)
assert isinstance(info, dict)
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_multi_agent(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0, stack=False, num_agents=2)
# Test for incorrect number of agents.
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)