基于价值一致性优先排序的高效多目标强化学习

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jiawei Xu, Shuxing Li, Rui Yang, Chun Yuan, Lei Han
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引用次数: 0

摘要

基于稀疏奖励的目标条件强化学习(RL)是深度强化学习中一个具有挑战性的问题。事后经验回放(HER)已被证明是一种有效的解决方案,HER将失败经验中的期望目标替换为实际实现的状态。现有的方法主要集中在探索或开发上,以提高HER的性能。从共同的角度来看,利用特定的过去经验也可以隐含地推动探索。因此,我们专注于对原始和重新标记的样本进行优先排序,以实现有效的目标条件强化学习。为了实现这一点,我们提出了一种新的值一致性优先化(VCP)方法,其中样本的优先级由集合q值的一致性决定。这将VCP方法与大多数现有的基于集合q值的不确定性对样本进行优先排序的方法区分开来。通过大量的实验,我们证明了VCP在一系列具有挑战性的目标条件操作任务上比现有算法实现了更高的样本效率。我们还可视化了VCP如何优先考虑好的经验来加强政策学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Multi-Goal Reinforcement Learning via Value Consistency Prioritization
Goal-conditioned reinforcement learning (RL) with sparse rewards remains a challenging problem in deep RL. Hindsight Experience Replay (HER) has been demonstrated to be an effective solution, where HER replaces desired goals in failed experiences with practically achieved states. Existing approaches mainly focus on either exploration or exploitation to improve the performance of HER. From a joint perspective, exploiting specific past experiences can also implicitly drive exploration. Therefore, we concentrate on prioritizing both original and relabeled samples for efficient goal-conditioned RL. To achieve this, we propose a novel value consistency prioritization (VCP) method, where the priority of samples is determined by the consistency of ensemble Q-values. This distinguishes the VCP method with most existing prioritization approaches which prioritizes samples based on the uncertainty of ensemble Q-values. Through extensive experiments, we demonstrate that VCP achieves significantly higher sample efficiency than existing algorithms on a range of challenging goal-conditioned manipulation tasks. We also visualize how VCP prioritizes good experiences to enhance policy learning.
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来源期刊
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research 工程技术-计算机:人工智能
CiteScore
9.60
自引率
4.00%
发文量
98
审稿时长
4 months
期刊介绍: JAIR(ISSN 1076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes. Established in 1993 as one of the first electronic scientific journals, JAIR is indexed by INSPEC, Science Citation Index, and MathSciNet. JAIR reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. JAIR articles are published for free distribution on the internet by the AI Access Foundation, and for purchase in bound volumes by AAAI Press.
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