一个因素化SARSA(λ)激励学习算法  

A FACTORED SARSA(λ)ALGORITHM OF REINFORCEMENT LEARNING

作　　者：陈焕文[1] 谢建平[2] 谢丽娟[1] 

机构地区：[1]长沙电力学院数学与计算机系,长沙410077 [2]长沙交通学院网络中心,长沙410076

出　　处：《计算机研究与发展》2001年第1期88-92,共5页Journal of Computer Research and Development

摘　　要：基于状态的因素化表达 ,提出了一个新的 SARSA(λ)激励学习算法 .其基本思想是根据状态的特征得出状态相似性启发式 ,再根据该启发式对状态空间进行聚类 ,大大减少了状态空间搜索与计算的复杂度 ,因此比较适用于求解大状态空间的 MDPs问题 .Based on the factored representation of a state, a new SARSA( λ ) algorithm is proposed. The main principle of the algorithm is that a heuristics on the state similarities can be gained from the features of the state, and according to the heuristics, the state space is aggregated, significantly reducing the searching and computing complexity for the state space. Therefore the algorithm is a promise for solving large scale MDPs problems which are of a huge state space.

关 键 词：激励学习 状态聚类 MDPs SARSA(λ)学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]