强化学习策略的低秩表示

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Bogdan Mazoure, T. Doan, Tianyu Li, V. Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau
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引用次数: 0

摘要

我们提出了一个用于强化学习任务的策略表示的通用框架。该框架涉及在再现核希尔伯特空间(RKHS)上寻找策略的低维嵌入。基于RKHS方法的使用使我们能够对重构政策的预期收益得出强有力的理论保证。这种保证在黑盒模型中通常是缺乏的,但是在需要稳定性和收敛性保证的任务中是非常理想的。我们在经典RL域上进行了几个实验。结果表明,策略在低维空间中可以鲁棒地表示,而嵌入策略几乎不会导致收益下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-Rank Representation of Reinforcement Learning Policies
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability and convergence guarantees. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly represented in a low-dimensional space while the embedded policy incurs almost no decrease in returns.
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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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