A Comprehensive Analysis of Game theory on Multi-Agent Reinforcement

In recent years, reinforcement learning has gradually emerged as a popular research field in artificial intelligence. However, for multi-agent systems, due to their complex environment and an abundance of agents, the learning target of maximizing the anticipated value of cumulative rewards for specific agents frequently fails to converge. Introducing game theory into reinforcement learning can effectively address the interactions among intelligent agents, provide a rationale for convergence points corresponding to strategies. To address the issue of non-existence of optimal solutions for certain tasks in multi-agent settings, this paper takes a game-theoretic perspective and summarizes classical reinforcement learning algorithms developed in recent years. The basic theory of multi-agent reinforcement learning, essential game theory, categorization of reinforcement learning using multiple agents game strategies, primary worries are addressed in the study. It also analyzes the challenges that game-theoretic multi-agent reinforcement learning algorithms may encounter in the future, along with the relevant optimization directions.