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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150 行
6.0 KiB

import json
import unittest.mock as mock
import pytest
import struct
import numpy as np
from unityagents import UnityEnvironment, UnityEnvironmentException, UnityActionException, \
BrainInfo, Curriculum
from .mock_communicator import MockCommunicator
dummy_curriculum = json.loads('''{
"measure" : "reward",
"thresholds" : [10, 20, 50],
"min_lesson_length" : 3,
"signal_smoothing" : true,
"parameters" :
{
"param1" : [0.7, 0.5, 0.3, 0.1],
"param2" : [100, 50, 20, 15],
"param3" : [0.2, 0.3, 0.7, 0.9]
}
}''')
bad_curriculum = json.loads('''{
"measure" : "reward",
"thresholds" : [10, 20, 50],
"min_lesson_length" : 3,
"signal_smoothing" : false,
"parameters" :
{
"param1" : [0.7, 0.5, 0.3, 0.1],
"param2" : [100, 50, 20],
"param3" : [0.2, 0.3, 0.7, 0.9]
}
}''')
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 * len(brain_info['RealFakeBrain'].agents))
with pytest.raises(UnityActionException):
env.step([0])
brain_info = env.step([-1] * brain.vector_action_space_size * len(brain_info['RealFakeBrain'].agents))
with pytest.raises(UnityActionException):
env.step([0] * brain.vector_action_space_size * 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
def test_curriculum():
open_name = '%s.open' % __name__
with mock.patch('json.load') as mock_load:
with mock.patch(open_name, create=True) as mock_open:
mock_open.return_value = 0
mock_load.return_value = bad_curriculum
with pytest.raises(UnityEnvironmentException):
Curriculum('tests/test_unityagents.py', {"param1": 1, "param2": 1, "param3": 1})
mock_load.return_value = dummy_curriculum
with pytest.raises(UnityEnvironmentException):
Curriculum('tests/test_unityagents.py', {"param1": 1, "param2": 1})
curriculum = Curriculum('tests/test_unityagents.py', {"param1": 1, "param2": 1, "param3": 1})
assert curriculum.get_lesson_number == 0
curriculum.set_lesson_number(1)
assert curriculum.get_lesson_number == 1
curriculum.increment_lesson(10)
assert curriculum.get_lesson_number == 1
curriculum.increment_lesson(30)
curriculum.increment_lesson(30)
assert curriculum.get_lesson_number == 1
assert curriculum.lesson_length == 3
curriculum.increment_lesson(30)
assert curriculum.get_config() == {'param1': 0.3, 'param2': 20, 'param3': 0.7}
assert curriculum.get_config(0) == {"param1": 0.7, "param2": 100, "param3": 0.2}
assert curriculum.lesson_length == 0
assert curriculum.get_lesson_number == 2
if __name__ == '__main__':
pytest.main()