This paper explores advanced topics in complex multi-agent systems building upon our previous work. We examine four fundamental challenges in Multi-Agent Reinforcement Learning (MARL): non-stationarity, partial observability, scalability with large agent populations, and decentralized learning. The paper provides mathematical formulations and analysis of recent algorithmic advancements designed to address these challenges, with a particular focus on their integration with game-theoretic concepts. We investigate how Nash equilibria, evolutionary game theory, correlated equilibrium, and adversarial dynamics can be effectively incorporated into MARL algorithms to improve learning outcomes. Through this comprehensive analysis, we demonstrate how the synthesis of game theory and MARL can enhance the robustness and effectiveness of multi-agent systems in complex, dynamic environments.

本研究解决了多智能体强化学习（MARL）中的四个基本挑战，包括非平稳性、部分可观测性、大规模智能体群体的可扩展性和分散学习。通过将博弈论概念与MARL算法相结合，该研究的关键发现是如何利用纳什均衡和进化博弈论的方法来增强多智能体系统在复杂动态环境中的鲁棒性和有效性。

博弈论与多智能体强化学习：从纳什均衡到进化动态