Review of multi-agent reinforcement learning under centralized training with decentralized execution
LI Yue-heng
XIE Guang-ming
Abstract：In recent years,multi-agent reinforcement learning(MARL)has gained significant attention due to its poten-tial in solving complex real-world problems.The centralized training with decentralized execution(CTDE)framework has been widely adopted in complex multi-agent systems.CTDE alleviates the non-stationarity problem in MARL by utilizing centralized training,but it also introduces new challenges,particularly in handling the information of all agents during training,especially the exponentially growing joint action space as the number of agents increases.This paper provides a selective review of the algorithms and developments in cooperative multi-agent reinforcement learning within the CTDE framework,focusing on two main approaches:Value function factorization methods and policy-based methods.This pa-per summarizes the performance of these algorithms in addressing the complexity of joint action spaces,non-stationarity,and estimation errors.It also proposes new perspectives and ideas,aiming to provide researchers in the field with deeper insights and guidance for future studies.
Keywords：multi-agent reinforcement learningcentralized training with decentralized executionvalue factorizationreinforcement learning
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：11( 2114-2124 )