Game Theory and Reinforcement Learning

Two Perspectives, One Frontier

Abstract

Reinforcement learning (RL) is a widely used learning paradigm that has shown significant successes in solving many hard AI problems including mastering real-time strategy games, autonomous car driving, and LLM model alignment. The formal mathematical framework underlying many of the problems solved by RL is game theory. However, these two areas are taught, and usually researched, independently of each other. In this tutorial, I will attempt to bridge this gap by introducing the fundamental concepts in RL and game theory and draw parallels between RL algorithm concepts like value updates, credit assignment, advantage and policy convergence, and their counterparts in game theory like backward induction, Nash equilibrium and regret.