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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from unittest.mock import MagicMock, patch
import pytest
from mlagents.tf_utils import tf
from mlagents.trainers.trainer_controller import TrainerController
from mlagents.trainers.sampler_class import SamplerManager
@pytest.fixture
def basic_trainer_controller():
return TrainerController(
trainer_factory=None,
model_path="test_model_path",
summaries_dir="test_summaries_dir",
run_id="test_run_id",
save_freq=100,
meta_curriculum=None,
train=True,
training_seed=99,
sampler_manager=SamplerManager({}),
resampling_interval=None,
)
@patch("numpy.random.seed")
@patch.object(tf, "set_random_seed")
def test_initialization_seed(numpy_random_seed, tensorflow_set_seed):
seed = 27
TrainerController(
trainer_factory=None,
model_path="",
summaries_dir="",
run_id="1",
save_freq=1,
meta_curriculum=None,
train=True,
training_seed=seed,
sampler_manager=SamplerManager({}),
resampling_interval=None,
)
numpy_random_seed.assert_called_with(seed)
tensorflow_set_seed.assert_called_with(seed)
@pytest.fixture
def trainer_controller_with_start_learning_mocks(basic_trainer_controller):
trainer_mock = MagicMock()
trainer_mock.get_step = 0
trainer_mock.get_max_steps = 5
trainer_mock.should_still_train = True
trainer_mock.parameters = {"some": "parameter"}
trainer_mock.write_tensorboard_text = MagicMock()
tc = basic_trainer_controller
tc.initialize_trainers = MagicMock()
tc.trainers = {"testbrain": trainer_mock}
tc.advance = MagicMock()
tc.trainers["testbrain"].get_step = 0
def take_step_sideeffect(env):
tc.trainers["testbrain"].get_step += 1
if (
not tc.trainers["testbrain"].get_step
<= tc.trainers["testbrain"].get_max_steps
):
tc.trainers["testbrain"].should_still_train = False
if tc.trainers["testbrain"].get_step > 10:
raise KeyboardInterrupt
return 1
tc.advance.side_effect = take_step_sideeffect
tc._export_graph = MagicMock()
tc._save_model = MagicMock()
return tc, trainer_mock
@patch.object(tf, "reset_default_graph")
def test_start_learning_trains_forever_if_no_train_model(
tf_reset_graph, trainer_controller_with_start_learning_mocks
):
tc, trainer_mock = trainer_controller_with_start_learning_mocks
tc.train_model = False
tf_reset_graph.return_value = None
env_mock = MagicMock()
env_mock.close = MagicMock()
env_mock.reset = MagicMock()
env_mock.external_brains = MagicMock()
tc.start_learning(env_mock)
tf_reset_graph.assert_called_once()
env_mock.reset.assert_called_once()
assert tc.advance.call_count == 11
tc._export_graph.assert_not_called()
tc._save_model.assert_not_called()
@patch.object(tf, "reset_default_graph")
def test_start_learning_trains_until_max_steps_then_saves(
tf_reset_graph, trainer_controller_with_start_learning_mocks
):
tc, trainer_mock = trainer_controller_with_start_learning_mocks
tf_reset_graph.return_value = None
brain_info_mock = MagicMock()
env_mock = MagicMock()
env_mock.close = MagicMock()
env_mock.reset = MagicMock(return_value=brain_info_mock)
env_mock.external_brains = MagicMock()
tc.start_learning(env_mock)
tf_reset_graph.assert_called_once()
env_mock.reset.assert_called_once()
assert tc.advance.call_count == trainer_mock.get_max_steps + 1
tc._save_model.assert_called_once()
@pytest.fixture
def trainer_controller_with_take_step_mocks(basic_trainer_controller):
trainer_mock = MagicMock()
trainer_mock.get_step = 0
trainer_mock.get_max_steps = 5
trainer_mock.parameters = {"some": "parameter"}
trainer_mock.write_tensorboard_text = MagicMock()
tc = basic_trainer_controller
tc.trainers = {"testbrain": trainer_mock}
tc.managers = {"testbrain": MagicMock()}
return tc, trainer_mock
def test_advance_adds_experiences_to_trainer_and_trains(
trainer_controller_with_take_step_mocks
):
tc, trainer_mock = trainer_controller_with_take_step_mocks
brain_name = "testbrain"
env_mock = MagicMock()
tc.brain_name_to_identifier[brain_name].add(brain_name)
tc.advance(env_mock)
env_mock.reset.assert_not_called()
env_mock.advance.assert_called_once()
trainer_mock.advance.assert_called_once()