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77 行
1.9 KiB
77 行
1.9 KiB
import yaml
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from unittest import mock
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import mlagents.trainers.tests.mock_brain as mb
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from mlagents.trainers.rl_trainer import RLTrainer
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from mlagents.trainers.tests.test_buffer import construct_fake_buffer
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def dummy_config():
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return yaml.safe_load(
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"""
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summary_path: "test/"
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summary_freq: 1000
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reward_signals:
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extrinsic:
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strength: 1.0
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gamma: 0.99
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"""
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)
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def create_mock_brain():
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mock_brain = mb.create_mock_brainparams(
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vector_action_space_type="continuous",
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vector_action_space_size=[2],
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vector_observation_space_size=8,
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number_visual_observations=1,
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)
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return mock_brain
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# Add concrete implementations of abstract methods
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class FakeTrainer(RLTrainer):
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def get_policy(self, name_behavior_id):
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return mock.Mock()
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def _is_ready_update(self):
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return True
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def _update_policy(self):
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pass
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def add_policy(self):
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pass
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def create_policy(self):
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return mock.Mock()
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def _process_trajectory(self, trajectory):
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super()._process_trajectory(trajectory)
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def create_rl_trainer():
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mock_brainparams = create_mock_brain()
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trainer = FakeTrainer(mock_brainparams, dummy_config(), True, 0)
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return trainer
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def test_rl_trainer():
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trainer = create_rl_trainer()
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agent_id = "0"
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trainer.episode_steps[agent_id] = 3
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trainer.collected_rewards["extrinsic"] = {agent_id: 3}
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# Test end episode
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trainer.end_episode()
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for agent_id in trainer.episode_steps:
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assert trainer.episode_steps[agent_id] == 0
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for rewards in trainer.collected_rewards.values():
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for agent_id in rewards:
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assert rewards[agent_id] == 0
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def test_clear_update_buffer():
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trainer = create_rl_trainer()
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trainer.update_buffer = construct_fake_buffer(0)
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trainer.clear_update_buffer()
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for _, arr in trainer.update_buffer.items():
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assert len(arr) == 0
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