U-Clustering:基于效用聚类的激励学习算法  

U-Clustering: A Reinforcement Learning Algorithm Based on Utility Clustering

作　　者：陈焕文[1] 殷苌茗[1] 谢丽娟[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院

出　　处：《计算机工程与应用》2005年第26期37-42,74,共7页Computer Engineering and Applications

基　　金：国家自然科学基金(编号:60075019)资助

摘　　要：提出了一个新的效用聚类激励学习算法U-Clustering。该算法完全不用像U-Tree算法那样进行边缘节点的生成和测试,它首先根据实例链的观测动作值对实例进行聚类,然后对每个聚类进行特征选择,最后再进行特征压缩,经过压缩后的新特征就成为新的状态空间树节点。通过对NewYorkDriving[2,13]的仿真和算法的实验分析,表明U-Clustering算法对解决大型部分可观测环境问题是比较有效的算法。That presented in this paper is a new utility clustering based reinforcement learning algorithm called U-Clustering.Unlike the U-Tree,it does not use fringe and related statistical test at all.The U-Clustering algorithm groups the instances that have matching history up to a certain length into a cluster based on the observation-action value of them,and makes the feature selecting and feature compressing for each cluster.The new features become new nodes in the agent＇s internal state space tree.Experimental results in a difficult partially observable driving task called New York Driving show that the U-Clustering algorithm is an effective one for solving the large partially observable problems.

关 键 词：激励学习 效用聚类 部分可观测Markov决策过程 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]