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欧亚空间:政策转移的增强行动空间
IEEE transactions on neural networks and learning systems
|November 7, 2023
概括
本研究介绍了增强行动空间 (EASpace),通过制定专家政策作为宏观行动来改善强化学习. EASpace加速学习,并增强复杂的,长时间的任务的探索.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 由于探索和信用分配效率低下,传统方法难以应对长时间的强化学习任务.
- 基于选项的多政策转移方法在探索宏观行动持续时间和利用长期行动方面存在局限性.
研究的目的:
- 提出一种新的算法,即增强行动空间 (EASpace),通过有效利用非最佳专家政策来加速强化学习.
- 通过新的宏观行动制定,应对长期任务中效率低下的勘探和利用不足的挑战.
主要方法:
- EASpace将每个专家政策制定成多个宏观行动,执行时间各不相同.
- 宏观行动直接集成到原始的行动空间.
- 引入了一种与宏观行动执行时间成比例的内在奖励,以激励剥削.
- 为了提高数据效率,采用类似于选项内Q学习的学习规则.
主要成果:
- 理论分析证实了拟议的学习规则的趋同.
- 在基于网格的游戏和多代理追逐问题中,EASpace表现出了效率.
- 通过在物理系统中的实现来验证算法的有效性.
结论:
- 通过结构化探索和改进学分分配,EASpace提供了一种有效的方法来加速强化学习.
- 该算法增强了有用的长期宏观行动的利用,克服了传统方法的局限性.
- 在复杂的控制任务和机器人技术中,EASpace显示出对现实世界应用的希望.
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