离线基于模型的可适应的政策学习,用于非支持地区的决策
IEEE transactions on pattern analysis and machine intelligence
|September 19, 2023
概括
本研究介绍了线下基于模型的可适应政策学习 (MAPLE),以改进数据集的强化学习. 在以前未有的情况下,MAPLE使可适应的政策能够做出强有力的决策,提高了超越当前方法的性能.
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:
- 线下强化学习 (RL) 提供了一种从现有数据集中学习政策的方法,避免了昂贵的在线探索.
- 当前的线下RL方法经常将政策限制在数据支持的地区,限制其潜在的表现.
- 这些限制可能会阻碍在遇到新情况时的适应能力和强度.
研究的目的:
- 调查线下RL在非支持区域的决策.
- 提出一种新的方法,即线下基于模型的可适应政策学习 (MAPLE),以克服支持区域限制的局限性.
- 制定一种能够适应的政策,能够调整未经探索的领域的行为.
主要方法:
- MAPLE学习了可适应的政策,可以将其泛化到不受支持的地区.
- 实际实施使用元学习和集合模型学习技术.
- 该方法侧重于在无支持地区的直接决策,而不是仅仅依赖支持数据.
主要成果:
- 在MuJoCo机动任务上的实验证明了MAPLE的能力.
- 该方法表明,在不受支持的地区,决策是强有力的.
- 与最先进的 (SOTA) 算法相比,MAPLE实现了更高的性能.
结论:
- 通过使政策能够适应不受支持的地区,MAPLE有效地解决了传统线下RL的局限性.
- 拟议的方法增强了离线政策学习的潜力.
- MAPLE提供了一个有前途的方向,用于从离线数据中开发更强大和更有能力的RL代理.
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