MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems

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	MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems
	Zhao, Dongbin; Zhu, Yuanheng
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2015-02-01
卷号	26 期号:2 页码:346-356
文章类型	Article
摘要	In this paper, the first probably approximately correct (PAC) algorithm for continuous deterministic systems without relying on any system dynamics is proposed. It combines the state aggregation technique and the efficient exploration principle, and makes high utilization of online observed samples. We use a grid to partition the continuous state space into different cells to save samples. A near-upper Q operator is defined to produce a near-upper Q function using samples in each cell. The corresponding greedy policy effectively balances between exploration and exploitation. With the rigorous analysis, we prove that there is a polynomial time bound of executing nonoptimal actions in our algorithm. After finite steps, the final policy reaches near optimal in the framework of PAC. The implementation requires no knowledge of systems and has less computation complexity. Simulation studies confirm that it is a better performance than other similar PAC algorithms.
关键词	Efficient Exploration Probably Approximately Correct (Pac) Reinforcement Learning (Rl) State Aggregation
WOS标题词	Science & Technology ; Technology
关键词[WOS]	TIME NONLINEAR-SYSTEMS ; MODEL-BASED EXPLORATION ; ZERO-SUM GAMES ; CONTROL SCHEME ; UNKNOWN DYNAMICS ; ITERATION ; STATE ; SPACES
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000348856200012
引用统计	被引频次：61[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8055
专题	多模态人工智能系统全国重点实验室_深度强化学习
作者单位	Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhao, Dongbin,Zhu, Yuanheng. MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(2):346-356.
APA	Zhao, Dongbin,&Zhu, Yuanheng.(2015).MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(2),346-356.
MLA	Zhao, Dongbin,et al."MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.2(2015):346-356.

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