关键对象对线下到线上强化学习的关键对应.
1Department of Computer Science, University of Illinois Chicago, Chicago, IL 60607, USA.
概括
本研究引入了一种用于深度离线强化学习的新方法,以提高模型性能. 通过后处理价值函数,它有效地服了分布之外的操作,简化了机器人代理的在线微调.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度离线增强学习 (RL) 利用数据集创建高质量的模型,减少在线微调需求.
- 在线RL中的国家行动分配转移可能会导致重大错误,降低最初的政策绩效.
- 当前的解决方案往往需要复杂的在线估计分布差异或密度比.
研究的目的:
- 为深度离线增强学习提出一种新的方法,以减轻分布转移造成的问题.
- 开发一种方法,通过后处理价值函数避免复杂的在线估计.
- 为了在关键参与者框架内实现更简单的在线微调程序.
主要方法:
- 偏离了直接转移状态-动作值函数的标准演员-关键方法.
- 后处理学到的价值功能与离线政策保持一致.
- 在已知的线下政策分布之外的行动中服Q值.
主要成果:
- 提出的方法成功地提高了微调机器人代理的性能.
- 经验结果表明,在各种模拟机器人任务中,性能得到了提高.
- 该方法简化了在线微调过程,使其与标准的演员-关键算法可比.
结论:
- 这种新的后处理技术有效地解决了离线RL中的分销转移所带来的挑战.
- 这种方法提供了一种更直接的方式,通过简化在线微调来实现高性能政策.
- 这种方法对机器人和其他依赖离线RL的领域的实用应用具有显著的希望.
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