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MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems
Zhao, Dongbin; Zhu, Yuanheng
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2015-02-01
Volume26Issue:2Pages:346-356
SubtypeArticle
AbstractIn 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.
KeywordEfficient Exploration Probably Approximately Correct (Pac) Reinforcement Learning (Rl) State Aggregation
WOS HeadingsScience & Technology ; Technology
WOS KeywordTIME NONLINEAR-SYSTEMS ; MODEL-BASED EXPLORATION ; ZERO-SUM GAMES ; CONTROL SCHEME ; UNKNOWN DYNAMICS ; ITERATION ; STATE ; SPACES
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000348856200012
Citation statistics
Cited Times:38[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8055
Collection复杂系统管理与控制国家重点实验室_深度强化学习
AffiliationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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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