CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 智能化团队
Online Reinforcement Learning by Bayesian Inference
Xia ZP(夏中谱); Dongbin Zhao
Conference NameInternational Joint Conference on Neural Networks
Source PublicationProceedings of International Joint Conference on Neural Networks 2015
Conference Date2015年7月
Conference PlaceIreland
AbstractPolicy evaluation has long been one of the core  issues of the online reinforcement learning, especially in the continuous state domain. In this paper, the issue is addressed by employing Gaussian processes to represent the action value function from the probability perspective. By modeling the return as a stochastic variable, the action value function can sequentially update according to observed variables such as state and reward by Bayesian inference during the policy evaluation. The update rule shows that it is a temporal difference learning method with the learning rate determined by the uncertainty of a collected sample. Incorporating the policy evaluation method with the E-greedy action selection method, we propose an online reinforcement learning algorithm referred as to Bayesian-SARSA. It is tested on some benchmark problems and the empirical results verifies its effectiveness.
KeywordReinforcement Learning Bayesian Inference Gaussian Processes
Indexed ByEI
Document Type会议论文
Corresponding AuthorDongbin Zhao
Recommended Citation
GB/T 7714
Xia ZP,Dongbin Zhao. Online Reinforcement Learning by Bayesian Inference[C],2015.
Files in This Item: Download All
File Name/Size DocType Version Access License
2015. IJCNN_XiaZhao.(751KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xia ZP(夏中谱)]'s Articles
[Dongbin Zhao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xia ZP(夏中谱)]'s Articles
[Dongbin Zhao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xia ZP(夏中谱)]'s Articles
[Dongbin Zhao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 2015. IJCNN_XiaZhao.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.