基于多智能体强化学习的对抗博弈技术综述

Review of Adversarial Game Techniques Based on Multi-Agent Reinforcement Learning

多智能体对抗系统是多方博弈的复杂系统。近年来,很多研究聚焦于用强化学习解决多智能体对抗博弈问题。文章从多智能体强化学习的角度对智能博弈对抗的算法进行综述。首先,简要介绍了对多智能体强化学习及博弈论;然后,提出多智能体强化学习的4项关键技术难点,并提出相关解决方法;最后,归纳多智能体强化学习的前沿研究方向,总结了研究热点与存在的挑战。综述为后续的研究打下基础,为使用多智能体强化学习解决博弈对抗问题提供思路。

Multi-agent adversarial systems are complex multi-perty game systems,and in recent years,many studies have focused on using reinforcement learning to solve multi-agent adversarial game problems.This article reviews intelligent game adversarial algorithms from the perspective of multi-agent reinforcement learning.First,a brief introduction to multi-agent reinforcement learning and game theory is given;then,four key technical difficulties of multi-agent reinforcement learning are proposed,and related solutions are sorted out;finally,the frontier research direction of multi-agent reinforcement learning is summarized,and three research hotspots and challenges are concluded.This review lays a foundation for the subsequent research and provides ideas for solving the game antagonism problem by using multi-agent reinforcement learning.