The Role of Reinforcement Learning in Game Theory

Introduction

Game theory is a field of study that deals with strategic decision-making in situations where the outcome of an individual’s choice depends on the choices of others. It has applications in various domains such as economics, political science, biology, and computer science. With the advent of advanced computing techniques, researchers have been able to apply reinforcement learning algorithms to game theory, enhancing our understanding of strategic interactions and enabling us to make better predictions about the behavior of rational agents. This article explores the role of reinforcement learning in game theory, focusing on its impact on both classical and new trends in computation and algorithms.