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 struct
import numpy as np
from unityagents import UnityEnvironment, UnityEnvironmentException, UnityActionException, \
BrainInfo
from .mock_communicator import MockCommunicator
def test_handles_bad_filename():
with pytest.raises(UnityEnvironmentException):
UnityEnvironment(' ')
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_initialization(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
with pytest.raises(UnityActionException):
env.step([0])
assert env.brain_names[0] == 'RealFakeBrain'
env.close()
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_reset(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
brain = env.brains['RealFakeBrain']
brain_info = env.reset()
env.close()
assert not env.global_done
assert isinstance(brain_info, dict)
assert isinstance(brain_info['RealFakeBrain'], BrainInfo)
assert isinstance(brain_info['RealFakeBrain'].visual_observations, list)
assert isinstance(brain_info['RealFakeBrain'].vector_observations, np.ndarray)
assert len(brain_info['RealFakeBrain'].visual_observations) == brain.number_visual_observations
assert brain_info['RealFakeBrain'].vector_observations.shape[0] == \
len(brain_info['RealFakeBrain'].agents)
assert brain_info['RealFakeBrain'].vector_observations.shape[1] == \
brain.vector_observation_space_size * brain.num_stacked_vector_observations
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_step(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
brain = env.brains['RealFakeBrain']
brain_info = env.reset()
brain_info = env.step([0] * brain.vector_action_space_size[0] * len(brain_info['RealFakeBrain'].agents))
with pytest.raises(UnityActionException):
env.step([0])
brain_info = env.step([-1] * brain.vector_action_space_size[0] * len(brain_info['RealFakeBrain'].agents))
with pytest.raises(UnityActionException):
env.step([0] * brain.vector_action_space_size[0] * len(brain_info['RealFakeBrain'].agents))
env.close()
assert env.global_done
assert isinstance(brain_info, dict)
assert isinstance(brain_info['RealFakeBrain'], BrainInfo)
assert isinstance(brain_info['RealFakeBrain'].visual_observations, list)
assert isinstance(brain_info['RealFakeBrain'].vector_observations, np.ndarray)
assert len(brain_info['RealFakeBrain'].visual_observations) == brain.number_visual_observations
assert brain_info['RealFakeBrain'].vector_observations.shape[0] == \
len(brain_info['RealFakeBrain'].agents)
assert brain_info['RealFakeBrain'].vector_observations.shape[1] == \
brain.vector_observation_space_size * brain.num_stacked_vector_observations
print("\n\n\n\n\n\n\n" + str(brain_info['RealFakeBrain'].local_done))
assert not brain_info['RealFakeBrain'].local_done[0]
assert brain_info['RealFakeBrain'].local_done[2]
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.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
if __name__ == '__main__':
pytest.main()