电子学报Issue(7)：1429-1434,6.DOI:10.3969/j.issn.0372-2112.2014.07.029
一种基于模型的可分解贝叶斯在线强化学习
Model-Based Factored Bayesian Online Reinforcement Learning
摘要
Abstract
Due to the enormous number of parameters and slow convergence which are the major obstacles for online learn -ing in model-based Bayesian reinforcement learning ,the paper presents a model-based factored Bayesian reinforcement learning ap-proach .Firstly ,factored representations are made to represent the dynamics with fewer parameters .Then ,according to prior knowl-edge and observable data ,this paper exploits model-based reinforcement learning to provide an elegant solution to the optimal explo-ration-exploitation tradeoff .Finally ,a pointed-based Bayesian reinforcement learning approach is proposed to speed up the conver -gence to achieve online learning .The experimental results show that the proposed approach can approximate the underlying Bayesian reinforcement learning task well with guaranteed real-time performance .关键词
马尔可夫决策过程/贝叶斯强化学习/动态贝叶斯网路Key words
Markov decision processes/Bayesian reinforcement learning/dynamic Bayesian networks分类
信息技术与安全科学引用本文复制引用
..一种基于模型的可分解贝叶斯在线强化学习[J].电子学报,2014,(7):1429-1434,6.基金项目
国家自然科学基金（No ．61074058，No ．60874042）；深圳市自然科学基金 （）
