Review of research on the integration of evolutionary game theory and multi-agent reinforcement learning
Abstract
Review of research on the integration of evolutionary game theory and multi-agent reinforcement learning
Incoming article date: 06.01.2026The paper provides an overview of research on the integration of evolutionary game theory (EGT) and multi-agent reinforcement learning (MARL). The main problems of MARL and the corresponding advantages of EGT are analyzed. As a result of the analysis, it was found that the implementation of EGT can effectively solve the problems of instability, credit allocation and partial observability in MARL, providing stable strategic convergence and a new path for group optimization. It is shown that the integration of EGT and MARL forms a promising theoretical and technical basis for a breakthrough in multi-agent control. At the same time, in order to deeply merge the two directions, integration mechanisms will have to be optimized in the future, more reliable algorithms will have to be developed, and applied research in complex heterogeneous systems will have to be strengthened.
Keywords: evolutionary game theory, multi-agent reinforcement learning, multi-agent control, instability, credit allocation, partial observability