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 patch, Mock
from mlagents.trainers.meta_curriculum import MetaCurriculum
from mlagents.trainers.tests.simple_test_envs import SimpleEnvironment
from mlagents.trainers.tests.test_simple_rl import (
_check_environment_trains,
BRAIN_NAME,
PPO_CONFIG,
)
from mlagents.trainers.tests.test_curriculum import dummy_curriculum_config
from mlagents.trainers.settings import CurriculumSettings
@pytest.fixture
def measure_vals():
return {"Brain1": 0.2, "Brain2": 0.3}
@pytest.fixture
def reward_buff_sizes():
return {"Brain1": 7, "Brain2": 8}
def test_curriculum_config(param_name="test_param1", min_lesson_length=100):
return CurriculumSettings(
thresholds=[0.1, 0.3, 0.5],
min_lesson_length=min_lesson_length,
parameters={f"{param_name}": [0.0, 4.0, 6.0, 8.0]},
)
test_meta_curriculum_config = {
"Brain1": test_curriculum_config("test_param1"),
"Brain2": test_curriculum_config("test_param2"),
}
def test_set_lesson_nums():
meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
meta_curriculum.lesson_nums = {"Brain1": 1, "Brain2": 3}
assert meta_curriculum.brains_to_curricula["Brain1"].lesson_num == 1
assert meta_curriculum.brains_to_curricula["Brain2"].lesson_num == 3
def test_increment_lessons(measure_vals):
meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
meta_curriculum.brains_to_curricula["Brain1"] = Mock()
meta_curriculum.brains_to_curricula["Brain2"] = Mock()
meta_curriculum.increment_lessons(measure_vals)
meta_curriculum.brains_to_curricula["Brain1"].increment_lesson.assert_called_with(
0.2
)
meta_curriculum.brains_to_curricula["Brain2"].increment_lesson.assert_called_with(
0.3
)
@patch("mlagents.trainers.curriculum.Curriculum")
@patch("mlagents.trainers.curriculum.Curriculum")
def test_increment_lessons_with_reward_buff_sizes(
curriculum_a, curriculum_b, measure_vals, reward_buff_sizes
):
curriculum_a.min_lesson_length = 5
curriculum_b.min_lesson_length = 10
meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
meta_curriculum.brains_to_curricula["Brain1"] = curriculum_a
meta_curriculum.brains_to_curricula["Brain2"] = curriculum_b
meta_curriculum.increment_lessons(measure_vals, reward_buff_sizes=reward_buff_sizes)
curriculum_a.increment_lesson.assert_called_with(0.2)
curriculum_b.increment_lesson.assert_not_called()
def test_set_all_curriculums_to_lesson_num():
meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
meta_curriculum.set_all_curricula_to_lesson_num(2)
assert meta_curriculum.brains_to_curricula["Brain1"].lesson_num == 2
assert meta_curriculum.brains_to_curricula["Brain2"].lesson_num == 2
def test_get_config():
meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
assert meta_curriculum.get_config() == {"test_param1": 0.0, "test_param2": 0.0}
@pytest.mark.parametrize("curriculum_brain_name", [BRAIN_NAME, "WrongBrainName"])
def test_simple_metacurriculum(curriculum_brain_name):
env = SimpleEnvironment([BRAIN_NAME], use_discrete=False)
mc = MetaCurriculum({curriculum_brain_name: dummy_curriculum_config})
_check_environment_trains(
env, {BRAIN_NAME: PPO_CONFIG}, meta_curriculum=mc, success_threshold=None
)