基于模糊聚类的分层强化学习算法  

A Hierarchical Reinforcement Learning Algorithm Based on Fuzzy Clustering

作　　者：张欣[1] 戴帅[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,湖南长沙410076

出　　处：《计算机工程与科学》2010年第1期55-56,91,共3页Computer Engineering & Science

基　　金：湖南省教委资助项目(07C083)

摘　　要：本文提出了一种新的分层强化学习Option自动生成算法,以Agent在学习初始阶段探测到的状态空间为输入,采用模糊逻辑神经元的网络进行聚类,在聚类后的各状态子集上通过经验回放学习产生内部策略集,生成Option,仿真实验结果表明了该算法的有效性。A new algorithm for the automatic generation of the Option Hierarchical Reinforcement Learning is presented. The algorithm takes the state space detected by the agent as input in the initial learning phase, and clusters the states by employing fuzzy clustering. Based on the clustered state sets, the intra-strategies are learned by an experience replay procedure. As a result, the options are generated. The validity of the algorithm is demonstrated by simulation experiments.

关 键 词：强化学习 分层强化学习 模糊聚类 OPTION 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]