MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems
Zhao, Dongbin; Zhu, Yuanheng
2015-02-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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
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被引频次:33[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
专题复杂系统管理与控制国家重点实验室_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
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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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