Reinforcement Learning for Game Theory and Strategy Optimization

Abstract

This article explores the integration of reinforcement learning (RL) into game theory and strategy optimization, focusing on how RL can enhance the decision-making process in competitive environments. RL, through its trial-and-error learning approach, has shown significant promise in developing strategies for both cooperative and non-cooperative games. By leveraging the power of RL, players or agents can continuously adapt and optimize their strategies based on dynamic game environments, providing new insights into game theory applications, particularly in economics, robotics, and artificial intelligence. This paper presents key concepts of RL and game theory, reviews relevant literature, and proposes frameworks for applying RL to solve strategic decision problems.