通过将状态可达性纳入强化学习,实现更有效的技能发现
Yang Liu1, Jingchen Li2, Huarui Wu2
1College of Optical Science and Engineering, Zhejiang University, Zhejiang Province, Hangzhou, 310058, China.
概括
本研究介绍了具有状态可达性的技能发现 (SDSR),以改善强化学习. SDSR确保学到的技能覆盖更多的州,增强适应新任务.
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:
- 强化学习 (RL) 中的技能发现旨在创造多样化的行为,以适应任务.
- 目前的方法往往缺乏状态可达性,限制了复杂环境中的适应性.
研究的目的:
- 提出一个新的框架,技能发现与状态可达性 (SDSR),将状态可达性整合到技能学习中.
- 通过确保国家全面覆盖,提高RL代理人适应下游任务的能力.
主要方法:
- 开发了SDSR,结合了技能条件逆动态模型来扩大可访问的状态空间.
- 引入了一种元政策优化机制,共同优化技能多样性和可达性.
- 通过基于门的选择和针对不同环境维度的联合培训实施SDSR.
主要成果:
- SDSR显著提高了技能多样性和勘探效率.
- 该框架加速了在2D和机器人环境中适应下游任务的速度.
- 在保持结构化技能多样性的同时,证明了可访问的州空间的扩张.
结论:
- 在复杂的决策中,SDSR为RL提供了强大而可通用的基础.
- 明确整合状态可达性克服了现有的技能发现方法的局限性.
- 提出的方法可以在多样化和具有挑战性的环境中提高RL剂的性能.
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