通过基于动态的行为相似性的学习表征,用于深度强化学习
1Department of Automation, Xiamen University, Xiamen, 361005, China.
概括
我们介绍了基于动态的行为相似性 (RDS) 的表示学习,以改善深度强化学习. 通过消除奖励依赖,RDS增强了表示学习,在复杂的操纵任务中实现了显著的性能增长.
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:
- 深度强化学习需要从视觉数据中学习与任务相关的表示.
- 行为相似度指标对等状态进行组合,但由于奖励稀少,代表性崩.
- 这限制了复杂应用程序的可扩展性.
研究的目的:
- 提出一种新的方法,即基于动态的行为相似性 (RDS) 的表示学习,以克服现有的表示学习方法的局限性.
- 开发一种独立于奖励的相似度指标,以保持行为歧视性,以改善深度强化学习.
主要方法:
- 引入了一个动态驱动的相似度指标,消除了奖励依赖.
- 集成的动态过渡距离与可训练的高斯噪声来缓解度量降解.
- 利用潜伏轨迹距离来量化任务差异和提取相关特征.
主要成果:
- 在复杂的DeepMind Control,MetaWorld和Aroit操纵任务上,RDS表现出比基线方法更好的性能.
- 与DrQ-v2和最先进的方法相比,分别实现了43%和30%的显著改进.
- 废弃性研究证实了RDS方法中的单个成分的有效性.
结论:
- 使用基于动态的行为相似性 (RDS) 的表示学习有效地解决了深度强化学习中的表示崩.
- 拟议的方法通过利用基于动态的相似性来增强对任务相关特征的学习,在具有挑战性的机器人任务上表现出强的表现.
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