The Value Function with Regret Minimization Algorithm for Solving the Nash Equilibrium of Multi-Agent Stochastic Game

Authors

Luping Liu, Wensheng Jia^*

College of Mathematics and Statistics, Guizhou University, Guizhou, Guiyang, 550025, China

^*Corresponding author. Email: wsjia@gzu.edu.cn

DOI

10.2991/ijcis.d.210520.001How to use a DOI?

Keywords

Regret minimization; Multi-agent; Stochastic game; Nash equilibrium; Spatial prisoner's dilemma

Abstract

In this paper, we study the value function with regret minimization algorithm for solving the Nash equilibrium of multi-agent stochastic game (MASG). To begin with, the idea of regret minimization is introduced to the value function, and the value function with regret minimization algorithm is designed. Furthermore, we analyze the effect of discount factor to the expected payoff. Finally, the single-agent stochastic game and spatial prisoner's dilemma (SDP) are investigated in order to support the theoretical results. The simulation results show that when the temptation parameter is small, the cooperation strategy is dominant; when the temptation parameter is large, the defection strategy is dominant. Therefore, we improve the level of cooperation between agents by setting appropriate temptation parameters.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Luping Liu
AU  - Wensheng Jia
PY  - 2021
DA  - 2021/05/26
TI  - The Value Function with Regret Minimization Algorithm for Solving the Nash Equilibrium of Multi-Agent Stochastic Game
JO  - International Journal of Computational Intelligence Systems
SP  - 1633
EP  - 1641
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210520.001
DO  - 10.2991/ijcis.d.210520.001
ID  - Liu2021
ER  -