计算机与数字工程2026，Vol.54Issue(6)：1553-1557,5.DOI:10.3969/j.issn.1672-9722.2026.06.002
基于值分解的2对2三国杀多智能体强化学习算法
A 2-on-2 Three-kill Multi-agent Reinforcement Learning Algorithm Based on Value Decomposition
摘要
Abstract
There are many problems in the multi-agent reinforcement learning environment,and the card-based multiplayer strategy game is a classic multi-agent system,which is always a problem that needs to be solved by game AI because of its unique characteristics,but there are few existing card game AI research,and most of them come from Dou Di Zhu,Texas Hold'em,Mah-jong,etc.In order to expand the research and application of reinforcement learning in card game AI,this paper proposes a three-kingdom killing multi-agent reinforcement learning method based on value decomposition,and self-builds a battle game scene with the three-kingdom killing game as the background as the environment of multiple agents,models the cooperative multi-ple agents,and solves the problem of instability in the environment of multiple agents.关键词
多智能体强化学习/三国杀游戏环境/合作对抗Key words
multi-agent reinforcement learning/three kingdoms killing game environment/cooperative competition分类
信息技术与安全科学引用本文复制引用
骆芙蓉,王以松,秦进,于小民..基于值分解的2对2三国杀多智能体强化学习算法[J].计算机与数字工程,2026,54(6):1553-1557,5.基金项目
国家自然科学基金项目(编号:U1836205)资助. （编号:U1836205）
