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 *
import yaml
import pytest
from mlagents.trainers.trainer_controller import TrainerController
from mlagents.envs.subprocess_env_manager import StepInfo
from mlagents.envs.sampler_class import SamplerManager
@pytest.fixture
def dummy_config():
return yaml.safe_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 basic_trainer_controller():
return TrainerController(
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,
fast_simulation=True,
sampler_manager=SamplerManager({}),
resampling_interval=None,
trainers={},
)
@patch("numpy.random.seed")
@patch("tensorflow.set_random_seed")
def test_initialization_seed(numpy_random_seed, tensorflow_set_seed):
seed = 27
TrainerController(
model_path="",
summaries_dir="",
run_id="1",
save_freq=1,
meta_curriculum=None,
train=True,
training_seed=seed,
fast_simulation=True,
sampler_manager=SamplerManager({}),
resampling_interval=None,
trainers={},
)
numpy_random_seed.assert_called_with(seed)
tensorflow_set_seed.assert_called_with(seed)
def trainer_controller_with_start_learning_mocks():
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.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 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("tensorflow.reset_default_graph")
def test_start_learning_trains_forever_if_no_train_model(tf_reset_graph):
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()
env_mock.close.assert_called_once()
@patch("tensorflow.reset_default_graph")
def test_start_learning_trains_until_max_steps_then_saves(tf_reset_graph):
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
env_mock.close.assert_called_once()
tc._save_model.assert_called_once()
def trainer_controller_with_take_step_mocks():
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}
return tc, trainer_mock
def test_take_step_adds_experiences_to_trainer_and_trains():
tc, trainer_mock = trainer_controller_with_take_step_mocks()
old_step_info = StepInfo(Mock(), Mock(), MagicMock())
new_step_info = StepInfo(Mock(), Mock(), MagicMock())
trainer_mock.is_ready_update = MagicMock(return_value=True)
env_mock = MagicMock()
env_mock.step.return_value = [new_step_info]
env_mock.reset.return_value = [old_step_info]
env_mock.global_done = False
tc.advance(env_mock)
env_mock.reset.assert_not_called()
env_mock.step.assert_called_once()
trainer_mock.add_experiences.assert_called_once_with(
new_step_info.previous_all_brain_info,
new_step_info.current_all_brain_info,
new_step_info.brain_name_to_action_info["testbrain"].outputs,
)
trainer_mock.process_experiences.assert_called_once_with(
new_step_info.previous_all_brain_info, new_step_info.current_all_brain_info
)
trainer_mock.update_policy.assert_called_once()
trainer_mock.increment_step.assert_called_once()