Multi-Agent Reinforcement Learning in non-zero-sum games: Algorithms and applications

Abstract

Multi-Agent Reinforcement Learning (MARL) algorithms have been utilized in resource allocation modelling, game theory analysis, formation of alliances and so on. The potential for future research and applications of MARL in non-zero-sum games is vast, including integrating MARL with game theory and mechanism design, developing online learning algorithms for dynamic environments, and exploring applications in various domains. Currently, there is a relative lack of review literature on this area of research. This paper, therefore, aims to fill this gap. This work will first introduce the role of MARL in non-zero-sum games. Then it will discuss in detail the practical applications of MARL in economics, social sciences, and political science, which are typical of non-zero-sum games while presenting possibilities and challenges for future research. Finally, a summary is given at the end of the paper. This article provides insights into the current and future possibilities of using MARL in non-zero-sum games.