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 pytest
import io
import os
from unittest.mock import patch
from mlagents.trainers import trainer_util
from mlagents.trainers.cli_utils import load_config, _load_config
from mlagents.trainers.ppo.trainer import PPOTrainer
from mlagents.trainers.exception import TrainerConfigError, UnityTrainerException
from mlagents.trainers.brain import BrainParameters
from mlagents.trainers.settings import RunOptions
from mlagents.trainers.tests.test_simple_rl import PPO_CONFIG
@pytest.fixture
def dummy_config():
return RunOptions(behaviors={"testbrain": PPO_CONFIG})
@patch("mlagents.trainers.brain.BrainParameters")
def test_initialize_ppo_trainer(BrainParametersMock, dummy_config):
brain_params_mock = BrainParametersMock()
BrainParametersMock.return_value.brain_name = "testbrain"
external_brains = {"testbrain": BrainParametersMock()}
run_id = "testrun"
output_path = "results_dir"
train_model = True
load_model = False
seed = 11
expected_reward_buff_cap = 1
base_config = dummy_config.behaviors
expected_config = PPO_CONFIG
def mock_constructor(
self, brain, reward_buff_cap, trainer_settings, training, load, seed, run_id
):
assert brain == brain_params_mock.brain_name
assert trainer_settings == expected_config
assert reward_buff_cap == expected_reward_buff_cap
assert training == train_model
assert load == load_model
assert seed == seed
assert run_id == run_id
with patch.object(PPOTrainer, "__init__", mock_constructor):
trainer_factory = trainer_util.TrainerFactory(
trainer_config=base_config,
run_id=run_id,
output_path=output_path,
train_model=train_model,
load_model=load_model,
seed=seed,
)
trainers = {}
for brain_name, brain_parameters in external_brains.items():
trainers[brain_name] = trainer_factory.generate(brain_parameters.brain_name)
assert "testbrain" in trainers
assert isinstance(trainers["testbrain"], PPOTrainer)
@patch("mlagents.trainers.brain.BrainParameters")
def test_handles_no_config_provided(BrainParametersMock):
"""
Make sure the trainer setup handles no configs provided at all.
"""
brain_name = "testbrain"
no_default_config = RunOptions().behaviors
brain_parameters = BrainParameters(
brain_name=brain_name,
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
trainer_factory = trainer_util.TrainerFactory(
trainer_config=no_default_config,
run_id="testrun",
output_path="output_path",
train_model=True,
load_model=False,
seed=42,
)
trainer_factory.generate(brain_parameters.brain_name)
def test_load_config_missing_file():
with pytest.raises(TrainerConfigError):
load_config("thisFileDefinitelyDoesNotExist.yaml")
def test_load_config_valid_yaml():
file_contents = """
this:
- is fine
"""
fp = io.StringIO(file_contents)
res = _load_config(fp)
assert res == {"this": ["is fine"]}
def test_load_config_invalid_yaml():
file_contents = """
you:
- will
- not
- parse
"""
with pytest.raises(TrainerConfigError):
fp = io.StringIO(file_contents)
_load_config(fp)
def test_existing_directories(tmp_path):
output_path = os.path.join(tmp_path, "runid")
# Test fresh new unused path - should do nothing.
trainer_util.handle_existing_directories(output_path, False, False)
# Test resume with fresh path - should throw an exception.
with pytest.raises(UnityTrainerException):
trainer_util.handle_existing_directories(output_path, True, False)
# make a directory
os.mkdir(output_path)
# Test try to train w.o. force, should complain
with pytest.raises(UnityTrainerException):
trainer_util.handle_existing_directories(output_path, False, False)
# Test try to train w/ resume - should work
trainer_util.handle_existing_directories(output_path, True, False)
# Test try to train w/ force - should work
trainer_util.handle_existing_directories(output_path, False, True)
# Test initialize option
init_path = os.path.join(tmp_path, "runid2")
with pytest.raises(UnityTrainerException):
trainer_util.handle_existing_directories(output_path, False, True, init_path)
os.mkdir(init_path)
# Should pass since the directory exists now.
trainer_util.handle_existing_directories(output_path, False, True, init_path)