A Neighborhood Correlated Empirical Weighted Algorithm for Fictitious Play
Fictitious play is a widely used learning model in games. In the fictitious play, players compute their best replies to opponents decisions. The empirical weighted fictitious play is an improved algorithm of the traditional fictitious play. This paper describes two disadvantages of the empirical weighted fictitious play. The first disadvantage is that distribution of the players own strategies may be important to make a strategy as times goes. The second is that all pairs of players selected from all players ignore their neighborhood information during playing games. This paper proposes a novel neighborhood correlated empirical weighted algorithm which adopts players own strategies and their neighborhood information. The comparison experiment results demonstrate that the neighborhood correlated empirical weighted algorithm can achieve a better convergence value.
Learning model Fictitious play Empirical weight Neighborhood information
Hongshu Wang Chunyan Yu Liqiao Wu
College of Mathematics & Computer Science, FuZhou University,Fuzhou, Fujian, China, 350108
国际会议
无锡
英文
305-311
2010-09-17（万方平台首次上网日期，不代表论文的发表时间）