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113 行
4.0 KiB
113 行
4.0 KiB
import pytest
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from unittest.mock import patch, Mock, call
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from mlagents.trainers.meta_curriculum import MetaCurriculum
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from mlagents.trainers.tests.simple_test_envs import SimpleEnvironment
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from mlagents.trainers.tests.test_simple_rl import (
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_check_environment_trains,
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BRAIN_NAME,
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PPO_CONFIG,
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)
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from mlagents.trainers.tests.test_curriculum import dummy_curriculum_config
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from mlagents.trainers.settings import CurriculumSettings
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from mlagents.trainers.training_status import StatusType
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@pytest.fixture
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def measure_vals():
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return {"Brain1": 0.2, "Brain2": 0.3}
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@pytest.fixture
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def reward_buff_sizes():
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return {"Brain1": 7, "Brain2": 8}
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def test_curriculum_config(param_name="test_param1", min_lesson_length=100):
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return CurriculumSettings(
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thresholds=[0.1, 0.3, 0.5],
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min_lesson_length=min_lesson_length,
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parameters={f"{param_name}": [0.0, 4.0, 6.0, 8.0]},
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)
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test_meta_curriculum_config = {
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"Brain1": test_curriculum_config("test_param1"),
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"Brain2": test_curriculum_config("test_param2"),
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}
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def test_set_lesson_nums():
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meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
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meta_curriculum.lesson_nums = {"Brain1": 1, "Brain2": 3}
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assert meta_curriculum.brains_to_curricula["Brain1"].lesson_num == 1
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assert meta_curriculum.brains_to_curricula["Brain2"].lesson_num == 3
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def test_increment_lessons(measure_vals):
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meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
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meta_curriculum.brains_to_curricula["Brain1"] = Mock()
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meta_curriculum.brains_to_curricula["Brain2"] = Mock()
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meta_curriculum.increment_lessons(measure_vals)
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meta_curriculum.brains_to_curricula["Brain1"].increment_lesson.assert_called_with(
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0.2
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)
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meta_curriculum.brains_to_curricula["Brain2"].increment_lesson.assert_called_with(
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0.3
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)
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@patch("mlagents.trainers.curriculum.Curriculum")
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@patch("mlagents.trainers.curriculum.Curriculum")
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def test_increment_lessons_with_reward_buff_sizes(
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curriculum_a, curriculum_b, measure_vals, reward_buff_sizes
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):
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curriculum_a.min_lesson_length = 5
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curriculum_b.min_lesson_length = 10
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meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
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meta_curriculum.brains_to_curricula["Brain1"] = curriculum_a
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meta_curriculum.brains_to_curricula["Brain2"] = curriculum_b
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meta_curriculum.increment_lessons(measure_vals, reward_buff_sizes=reward_buff_sizes)
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curriculum_a.increment_lesson.assert_called_with(0.2)
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curriculum_b.increment_lesson.assert_not_called()
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@patch("mlagents.trainers.meta_curriculum.GlobalTrainingStatus")
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def test_restore_curriculums(mock_trainingstatus):
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meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
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# Test restore to value
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mock_trainingstatus.get_parameter_state.return_value = 2
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meta_curriculum.try_restore_all_curriculum()
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mock_trainingstatus.get_parameter_state.assert_has_calls(
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[call("Brain1", StatusType.LESSON_NUM), call("Brain2", StatusType.LESSON_NUM)],
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any_order=True,
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)
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assert meta_curriculum.brains_to_curricula["Brain1"].lesson_num == 2
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assert meta_curriculum.brains_to_curricula["Brain2"].lesson_num == 2
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# Test restore to None
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mock_trainingstatus.get_parameter_state.return_value = None
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meta_curriculum.try_restore_all_curriculum()
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assert meta_curriculum.brains_to_curricula["Brain1"].lesson_num == 0
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assert meta_curriculum.brains_to_curricula["Brain2"].lesson_num == 0
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def test_get_config():
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meta_curriculum = MetaCurriculum(test_meta_curriculum_config)
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assert meta_curriculum.get_config() == {"test_param1": 0.0, "test_param2": 0.0}
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@pytest.mark.parametrize("curriculum_brain_name", [BRAIN_NAME, "WrongBrainName"])
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def test_simple_metacurriculum(curriculum_brain_name):
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env = SimpleEnvironment([BRAIN_NAME], use_discrete=False)
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mc = MetaCurriculum({curriculum_brain_name: dummy_curriculum_config})
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_check_environment_trains(
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env, {BRAIN_NAME: PPO_CONFIG}, meta_curriculum=mc, success_threshold=None
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)
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