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 json
import unittest.mock as mock
import yaml
import pytest
import tensorflow as tf
from mlagents.trainers.trainer_controller import TrainerController
from mlagents.trainers.ppo.trainer import PPOTrainer
from mlagents.trainers.bc.offline_trainer import OfflineBCTrainer
from mlagents.trainers.bc.online_trainer import OnlineBCTrainer
from mlagents.envs.exception import UnityEnvironmentException
from tests.mock_communicator import MockCommunicator
@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_online_bc_config():
return yaml.load(
'''
default:
trainer: online_bc
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_offline_bc_config():
return yaml.load(
'''
default:
trainer: offline_bc
demo_path: ./tests/trainers/test.demo
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_offline_bc_config, dummy_online_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, False, 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()
tc._initialize_trainers(config)
assert (len(tc.trainers) == 1)
assert (isinstance(tc.trainers['RealFakeBrain'], PPOTrainer))
# Test for Online Behavior Cloning Trainer
mock_load.return_value = dummy_online_bc_config
config = tc._load_config()
tf.reset_default_graph()
tc._initialize_trainers(config)
assert (isinstance(tc.trainers['RealFakeBrain'], OnlineBCTrainer))
# 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 pytest.raises(UnityEnvironmentException):
tc._initialize_trainers(config)
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_initialize_offline_trainers(mock_communicator, mock_launcher, dummy_config,
dummy_offline_bc_config, dummy_online_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=False, stack=False, visual_inputs=0,
brain_name="Ball3DBrain", vec_obs_size=8)
tc = TrainerController(' ', ' ', 1, None, True, False, False, 1, 1,
1, 1, '', "tests/test_mlagents.trainers.py",
False)
# Test for Offline Behavior Cloning Trainer
mock_load.return_value = dummy_offline_bc_config
config = tc._load_config()
tf.reset_default_graph()
tc._initialize_trainers(config)
assert (isinstance(tc.trainers['Ball3DBrain'], OfflineBCTrainer))