基于变分自由能的高斯过程回归强化学习

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2023-01-01 DOI:10.1515/jisys-2022-0205
Kiseki Kameda, F. Tanaka
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引用次数: 0

摘要

现有使用高斯过程回归的强化学习算法的核心部分是复杂的在线高斯过程回归算法。我们的研究提出了在线和小批量高斯过程回归算法,更容易实现,更快地估计强化学习。在我们的算法中,高斯过程回归仅通过计算两个方程来更新值函数,然后我们使用它们来构建强化学习算法。数值实验表明,本文提出的算法与已有的算法一样有效。
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Reinforcement learning with Gaussian process regression using variational free energy
Abstract The essential part of existing reinforcement learning algorithms that use Gaussian process regression involves a complicated online Gaussian process regression algorithm. Our study proposes online and mini-batch Gaussian process regression algorithms that are easier to implement and faster to estimate for reinforcement learning. In our algorithm, the Gaussian process regression updates the value function through only the computation of two equations, which we then use to construct reinforcement learning algorithms. Our numerical experiments show that the proposed algorithm works as well as those from previous studies.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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