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
from unittest.mock import MagicMock, patch
from mlagents.trainers import learn
from mlagents.trainers.trainer_controller import TrainerController
from mlagents.trainers.learn import parse_command_line
def basic_options(extra_args=None):
extra_args = extra_args or {}
args = ["basic_path"]
if extra_args:
args += [f"{k}={v}" for k, v in extra_args.items()]
return parse_command_line(args)
@patch("mlagents.trainers.learn.TrainerFactory")
@patch("mlagents.trainers.learn.SamplerManager")
@patch("mlagents.trainers.learn.SubprocessEnvManager")
@patch("mlagents.trainers.learn.create_environment_factory")
@patch("mlagents.trainers.learn.load_config")
def test_run_training(
load_config,
create_environment_factory,
subproc_env_mock,
sampler_manager_mock,
trainer_factory_mock,
):
mock_env = MagicMock()
mock_env.external_brain_names = []
mock_env.academy_name = "TestAcademyName"
create_environment_factory.return_value = mock_env
trainer_config_mock = MagicMock()
load_config.return_value = trainer_config_mock
mock_init = MagicMock(return_value=None)
with patch.object(TrainerController, "__init__", mock_init):
with patch.object(TrainerController, "start_learning", MagicMock()):
learn.run_training(0, 0, basic_options(), MagicMock())
mock_init.assert_called_once_with(
trainer_factory_mock.return_value,
"./models/ppo-0",
"./summaries",
"ppo-0",
50000,
None,
False,
0,
sampler_manager_mock.return_value,
None,
)
@patch("mlagents.trainers.learn.SamplerManager")
@patch("mlagents.trainers.learn.SubprocessEnvManager")
@patch("mlagents.trainers.learn.create_environment_factory")
@patch("mlagents.trainers.learn.load_config")
def test_docker_target_path(
load_config, create_environment_factory, subproc_env_mock, sampler_manager_mock
):
mock_env = MagicMock()
mock_env.external_brain_names = []
mock_env.academy_name = "TestAcademyName"
create_environment_factory.return_value = mock_env
trainer_config_mock = MagicMock()
load_config.return_value = trainer_config_mock
options_with_docker_target = basic_options({"--docker-target-name": "dockertarget"})
mock_init = MagicMock(return_value=None)
with patch.object(TrainerController, "__init__", mock_init):
with patch.object(TrainerController, "start_learning", MagicMock()):
learn.run_training(0, 0, options_with_docker_target, MagicMock())
mock_init.assert_called_once()
assert mock_init.call_args[0][1] == "/dockertarget/models/ppo-0"
assert mock_init.call_args[0][2] == "/dockertarget/summaries"
def test_commandline_args():
# No args raises
with pytest.raises(SystemExit):
parse_command_line([])
# Test with defaults
opt = parse_command_line(["mytrainerpath"])
assert opt.trainer_config_path == "mytrainerpath"
assert opt.env_path is None
assert opt.curriculum_folder is None
assert opt.sampler_file_path is None
assert opt.keep_checkpoints == 5
assert opt.lesson == 0
assert opt.load_model is False
assert opt.run_id == "ppo"
assert opt.save_freq == 50000
assert opt.seed == -1
assert opt.train_model is False
assert opt.base_port == 5005
assert opt.num_envs == 1
assert opt.docker_target_name is None
assert opt.no_graphics is False
assert opt.debug is False
assert opt.multi_gpu is False
assert opt.env_args is None
full_args = [
"mytrainerpath",
"--env=./myenvfile",
"--curriculum=./mycurriculum",
"--sampler=./mysample",
"--keep-checkpoints=42",
"--lesson=3",
"--load",
"--run-id=myawesomerun",
"--num-runs=3",
"--save-freq=123456",
"--seed=7890",
"--train",
"--base-port=4004",
"--num-envs=2",
"--docker-target-name=mydockertarget",
"--no-graphics",
"--debug",
"--multi-gpu",
]
opt = parse_command_line(full_args)
assert opt.trainer_config_path == "mytrainerpath"
assert opt.env_path == "./myenvfile"
assert opt.curriculum_folder == "./mycurriculum"
assert opt.sampler_file_path == "./mysample"
assert opt.keep_checkpoints == 42
assert opt.lesson == 3
assert opt.load_model is True
assert opt.run_id == "myawesomerun"
assert opt.save_freq == 123456
assert opt.seed == 7890
assert opt.train_model is True
assert opt.base_port == 4004
assert opt.num_envs == 2
assert opt.docker_target_name == "mydockertarget"
assert opt.no_graphics is True
assert opt.debug is True
assert opt.multi_gpu is True
def test_env_args():
full_args = [
"mytrainerpath",
"--env=./myenvfile",
"--env-args", # Everything after here will be grouped in a list
"--foo=bar",
"--blah",
"baz",
"100",
]
opt = parse_command_line(full_args)
assert opt.env_args == ["--foo=bar", "--blah", "baz", "100"]