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D3HRL:一种基于因果发现和虚假相关性检测的分布式等级强化学习方法
Chenran Zhao1, Dianxi Shi2, Mengzhu Wang3
1College of Computer Science and Technology, National University of Defense Technology, Changsha, China.
概括
本研究介绍了D3HRL,一种因果层次强化学习 (HRL) 方法. 在复杂的决策任务中,D3HRL有效地解决了延迟效应和虚假相关性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 因果推理因果推理
背景情况:
- 层次增强学习 (HRL) 在长期的顺序决策方面表现出色.
- 现有的HRL方法在延迟效应和虚假相关性方面扎.
- 这些挑战阻碍了复杂环境中可靠的决策.
研究的目的:
- 提出一种新的因果层次强化学习 (HRL) 方法,D3HRL.
- 解决和减轻HRL延迟效应和虚假相关性的影响.
- 在复杂的连续任务中提高决策可靠性.
主要方法:
- D3HRL模型将效应作为不同时间跨度的因果关系延迟.
- 分布因果发现被用来学习这些因果关系.
- 条件独立性测试是用来消除虚假的相关性,然后是针对已识别的因果关系进行政策培训.
主要成果:
- 在实验环境中,D3HRL表现出对延迟效应的优越灵敏度.
- 拟议的方法准确地识别了任务中的真正因果关系.
- 在2D-MineCraft和MiniGrid中的实验验验证了D3HRL的有效性.
结论:
- D3HRL为层次强化学习的挑战提供了一个强大的解决方案.
- 因果关系方法导致在复杂环境中更可靠的决策.
- 因果链的反复改进提高了探索和政策绩效.
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