将博弈论融入强化学习，提升多智能体系统在动态环境中的鲁棒性。
Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics
- 用纳什均衡、演化博弈等理论指导多智能体学习策略设计。
- 解决非平稳性、部分可观测性等四大核心挑战，提升算法稳定性。
- 适合研究多智能体协作与对抗的学者参考。
本文在前期工作基础上，深入探讨复杂多智能体系统中的四个关键挑战：非平稳性、部分可观测性、大规模智能体群体下的可扩展性以及去中心化学习。论文提供了近期算法进展的数学建模与分析，特别关注其与博弈论概念的融合。研究揭示了纳什均衡、演化博弈理论、相关均衡及对抗动态如何有效整合进多智能体强化学习算法，以改善学习效果。通过系统性分析，证明博弈论与多智能体强化学习的结合能显著增强复杂动态环境中多智能体系统的鲁棒性与有效性。
原文摘要 · Abstract (English)
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.
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