The Cooperation and Competition Mechanisms In Multi-agent Reinforcement Learning

With the development of artificial intelligence, multi-agent systems have become an important research hotspot for achieving autonomous multi-agent collaboration and intelligent decision-making. Among them, multi-agent reinforcement learning (MARL) has become a key framework for solving uncertain and dynamic interaction problems. This article systematically reviews the cooperation and competition mechanisms in MARL. The article points out that centralized training and decentralized execution (CTDE) is the core paradigm for solving the problems of credit allocation and environmental instability in cooperation, and has derived key technologies such as value decomposition and actor-critic. For competition, the combination of game theory and deep reinforcement learning provides a theoretical basis for strategic interaction. Additionally, the article analyzes the complexity of mixed cooperative-competitive scenarios, summarizes a comprehensive framework integrating multiple technologies, and demonstrates its application potential through cases such as games and smart grids. Finally, in response to current bottlenecks, it looks forward to future directions such as combining with large models and improving generalization ability.