Applications of multi-agent reinforcement learning in differential games
LÜ Meng-xin
SHI Zong-ying
ZHONG Yi-sheng
Abstract：Differential games offer a powerful framework for modeling and analyzing the decision-making problems in multi-agent systems under competitive environments,with extensive application prospects in fields like economics and industry.However,as the problem modeling approaches real-world scenarios,obtaining theoretical solutions becomes increasingly challenging.In recent years,with the rapid advancement of artificial intelligence,multi-agent deep rein-forcement learning has achieved significant breakthroughs,providing promising alternatives for addressing the challenges encountered in differential games.This paper firstly provides a comprehensive review of the fundamental principles and lat-est development of both fields.The paper further details the reinforcement learning methods applied to differential games,categorizing them based on the problem formulations and deep-learning network architectures,and illustrates how recent researches overcome the challenges faced by traditional methods.Finally,the paper summarizes the current progress in this interdisciplinary research area and suggests potential future directions.
Keywords：reinforcement learningdifferential gamesmult-agent systemsoptimal control systems
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：14( 2165-2178 )