Online Reinforcement Learning by Bayesian Inference
文献类型：会议论文
| 作者 | Xia ZP(夏中谱) ; Dongbin Zhao
|
| 出版日期 | 2015-07 |
| 会议日期 | 2015年7月 |
| 会议地点 | Ireland |
| 关键词 | Reinforcement Learning Bayesian Inference Gaussian Processes |
| 英文摘要 | Policy 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. |
| 会议录 | Proceedings of International Joint Conference on Neural Networks 2015
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/11434]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 通讯作者 | Dongbin Zhao |
| 推荐引用方式 GB/T 7714 | Xia ZP,Dongbin Zhao. Online Reinforcement Learning by Bayesian Inference[C]. 见:. Ireland. 2015年7月. |
入库方式： OAI收割
来源：自动化研究所
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