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。
您最多选择25个主题 主题必须以中文或者字母或数字开头,可以包含连字符 (-),并且长度不得超过35个字符
 
 
 
 
 

188 行
6.5 KiB

import json
import unittest.mock as mock
import yaml
import pytest
import tensorflow as tf
from mlagents.trainers.trainer_controller import TrainerController
from mlagents.trainers.buffer import Buffer
from mlagents.trainers.ppo.trainer import PPOTrainer
from mlagents.trainers.bc.trainer import BehavioralCloningTrainer
from mlagents.trainers.curriculum import Curriculum
from mlagents.trainers.exception import CurriculumError
from mlagents.envs.exception import UnityEnvironmentException
from tests.mock_communicator import MockCommunicator
@pytest.fixture
def dummy_start():
return '''{ "AcademyName": "RealFakeAcademy",
"resetParameters": {},
"brainNames": ["RealFakeBrain"],
"externalBrainNames": ["RealFakeBrain"],
"logPath":"RealFakePath",
"apiNumber":"API-5",
"brainParameters": [{
"vectorObservationSize": 3,
"numStackedVectorObservations" : 2,
"vectorActionSize": 2,
"memorySize": 0,
"cameraResolutions": [],
"vectorActionDescriptions": ["",""],
"vectorActionSpaceType": 1
}]
}'''.encode()
@pytest.fixture
def dummy_config():
return yaml.load(
'''
default:
trainer: ppo
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
gamma: 0.99
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
memory_size: 8
use_curiosity: false
curiosity_strength: 0.0
curiosity_enc_size: 1
''')
@pytest.fixture
def dummy_bc_config():
return yaml.load(
'''
default:
trainer: imitation
brain_to_imitate: ExpertBrain
batches_per_epoch: 16
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
gamma: 0.99
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
memory_size: 8
use_curiosity: false
curiosity_strength: 0.0
curiosity_enc_size: 1
''')
@pytest.fixture
def dummy_bad_config():
return yaml.load(
'''
default:
trainer: incorrect_trainer
brain_to_imitate: ExpertBrain
batches_per_epoch: 16
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
gamma: 0.99
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
memory_size: 8
''')
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_initialization(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=1)
tc = TrainerController(' ', ' ', 1, None, True, True, False, 1,
1, 1, 1, '', "tests/test_mlagents.trainers.py", False)
assert(tc.env.brain_names[0] == 'RealFakeBrain')
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_load_config(mock_communicator, mock_launcher, dummy_config):
open_name = 'mlagents.trainers.trainer_controller' + '.open'
with mock.patch('yaml.load') as mock_load:
with mock.patch(open_name, create=True) as _:
mock_load.return_value = dummy_config
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=1)
mock_load.return_value = dummy_config
tc = TrainerController(' ', ' ', 1, None, True, True, False, 1,
1, 1, 1, '','', False)
config = tc._load_config()
assert(len(config) == 1)
assert(config['default']['trainer'] == "ppo")
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_initialize_trainers(mock_communicator, mock_launcher, dummy_config,
dummy_bc_config, dummy_bad_config):
open_name = 'mlagents.trainers.trainer_controller' + '.open'
with mock.patch('yaml.load') as mock_load:
with mock.patch(open_name, create=True) as _:
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=1)
tc = TrainerController(' ', ' ', 1, None, True, True, False, 1, 1,
1, 1, '', "tests/test_mlagents.trainers.py",
False)
# Test for PPO trainer
mock_load.return_value = dummy_config
config = tc._load_config()
tf.reset_default_graph()
with tf.Session() as sess:
tc._initialize_trainers(config, sess)
assert(len(tc.trainers) == 1)
assert(isinstance(tc.trainers['RealFakeBrain'], PPOTrainer))
# Test for Behavior Cloning Trainer
mock_load.return_value = dummy_bc_config
config = tc._load_config()
tf.reset_default_graph()
with tf.Session() as sess:
tc._initialize_trainers(config, sess)
assert(isinstance(tc.trainers['RealFakeBrain'], BehavioralCloningTrainer))
# Test for proper exception when trainer name is incorrect
mock_load.return_value = dummy_bad_config
config = tc._load_config()
tf.reset_default_graph()
with tf.Session() as sess:
with pytest.raises(UnityEnvironmentException):
tc._initialize_trainers(config, sess)