Knowledge Commons of Institute of Automation,CAS
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 | |
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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
06971146.pdf(2156KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
个性服务 |
推荐该条目 |
保存到收藏夹 |
查看访问统计 |
导出为Endnote文件 |
谷歌学术 |
谷歌学术中相似的文章 |
[Zhao, Dongbin]的文章 |
[Zhu, Yuanheng]的文章 |
百度学术 |
百度学术中相似的文章 |
[Zhao, Dongbin]的文章 |
[Zhu, Yuanheng]的文章 |
必应学术 |
必应学术中相似的文章 |
[Zhao, Dongbin]的文章 |
[Zhu, Yuanheng]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论