Mastering Chess with Deep Learning: A Monte Carlo Tree Search and PPO Loss Approach

I. Introduction

A. Brief overview of the project

In this article, I present an innovative Deep Learning network designed to play chess with exceptional skill. By leveraging the power of advanced artificial intelligence techniques, this project aims to push the boundaries of what is possible in the realm of chess engines. At the heart of this network lies a unique combination of Monte Carlo Tree Search and Proximal Policy Optimization (PPO) loss, which allows the system to learn and adapt its strategies to achieve superior performance.

B. Connection to chess as a classic testbed for AI

Chess has long been regarded as the ultimate intellectual battleground and an ideal testbed for artificial intelligence. Since the dawn of the computer age, researchers have been fascinated with the idea of creating a machine capable of defeating human chess masters. Over the years, chess engines have evolved from simple rule-based systems to sophisticated algorithms that can challenge even the world’s best players.

C. The role of deep learning in modern chess engines

With the recent advancements in deep learning and artificial intelligence, modern chess engines have undergone a significant transformation. Deep learning has enabled these engines to not only analyze…