How do we mathematically formulate the problem of learning to play a game as complex as chess? In the first part of this two-part series, we will approach chess through the lens of Reinforcement Learning and Stochastic Approximation. We begin by formalizing the game as a two-player zero-sum Markov Decision Process, discussing the Bellman Optimality Equation and minimax equilibria.
