The Efficacy of Choosing Strategy with General Regression Neural Network on Evolutionary Markov Games

Abstract

Nowadays, Evolutionary Game Theory which studies the learning model of players, has attracted more attention than before. These Games can simulate the real situation and dynamic during processing time. This paper creates the Evolutionary Markov Games, which maps players’ strategy-choosing to a Markov Decision Processes (MDPs) with payoffs. Boltzmann distribution is used for transition probability and the General Regression Neural Network (GRNN) simulating the strategy-choosing in Evolutionary Markov Games. Prisoner’s dilemma is a problem that uses the method and output results showing the overlapping the human strategy-choosing line and GRNN strategy-choosing line after 48 iterations, and they choose the same strategies. Also, the error rate of the GRNN training by Tit for Tat (TFT) strategy is lower than similar work and shows a better result.