Offline reinforcement learning with representations for actions
文献类型：期刊论文
| 作者 | Lou, Xingzhou4,5; Yin, Qiyue4 ; Zhang, Junge4; Yu, Chao1; He, Zhaofeng2; Cheng, Nengjie3; Huang, Kaiqi4
|
| 刊名 | INFORMATION SCIENCES
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 610页码:746-758 |
| ISSN号 | 0020-0255 |
| 关键词 | Offline reinforcement learning Action embedding |
| DOI | 10.1016/j.ins.2022.08.019 |
| 通讯作者 | Zhang, Junge() |
| 英文摘要 | Prevailing offline reinforcement learning (RL) methods limit the policy within the area sup-ported by the offline dataset to avoid the distributional shift problem. But potential high -reward actions, which are out of the distribution of the dataset, are neglected in these meth-ods. To address such issue, we propose a new method, which generalizes from the offline dataset to out-of-distribution (OOD) actions. Specifically, we design a novel action embed-ding model to help infer the effect of actions. As a result, our value function reaches a better generalization over the action space, and further alleviate the distributional shift caused by overestimation of OOD actions. Theoretically, we give an information-theoretic explanation on the improvement of the value function's generalization over the action space. Experiments on D4RL demonstrate that our model improves the performance compared to previous offline RL methods, especially when the experience in the offline dataset is good. We conduct further study and validate that the value function's generalization on OOD actions is improved, which reinforces the effectiveness of our proposed action embedding model. (c) 2022 Published by Elsevier Inc. |
| 资助项目 | National Natural Science Foundation of China[61876181] ; Beijing Nova Program of Science and Technology[Z191100001119043] ; Youth Innovation Promotion Association, CAS |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:000860782400007 |
| 资助机构 | National Natural Science Foundation of China ; Beijing Nova Program of Science and Technology ; Youth Innovation Promotion Association, CAS |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50376]  |
| 专题 | 智能系统与工程 |
| 通讯作者 | Zhang, Junge |
| 作者单位 | 1.Sun Yat Sen Univ, Guangzhou, Peoples R China 2.Beijing Univ Posts & Telecommun, Beijing, Peoples R China 3.Nanchang Univ, Nanchang, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lou, Xingzhou,Yin, Qiyue,Zhang, Junge,et al. Offline reinforcement learning with representations for actions[J]. INFORMATION SCIENCES,2022,610:746-758. |
| APA | Lou, Xingzhou.,Yin, Qiyue.,Zhang, Junge.,Yu, Chao.,He, Zhaofeng.,...&Huang, Kaiqi.(2022).Offline reinforcement learning with representations for actions.INFORMATION SCIENCES,610,746-758. |
| MLA | Lou, Xingzhou,et al."Offline reinforcement learning with representations for actions".INFORMATION SCIENCES 610(2022):746-758. |
入库方式： OAI收割
来源：自动化研究所
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