The Value of Chess Squares

TL;DR

This paper introduces a neural network model to evaluate the strategic value of specific chess square-piece combinations, enhancing traditional fixed-value assessments with learned, marginal valuations.

Contribution

It presents a novel neural network approach using deep Q-learning to dynamically assess the worth of pieces on specific squares in chess positions.

Findings

Accurately assesses the value of pieces on specific squares.

Provides insights into the valuation of Knights, Bishops, and pawns.

Enhances traditional fixed-value chess assessments.

Abstract

We propose a neural network-based approach to calculate the value of a chess square-piece combination. Our model takes a triplet (Color, Piece, Square) as an input and calculates a value that measures the advantage/disadvantage of having this piece on this square. Our methods build on recent advances in chess AI, and can accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces $(\symking=\infty, \symqueen=9, \symrook=5, \symbishop=3, \symknight=3, \sympawn=1)$. We enhance this analysis by introducing marginal valuations. We use deep Q-learning to estimate the parameters of our model. We demonstrate our method by examining the positioning of Knights and Bishops, and also provide valuable insights into the valuation of pawns. Finally, we conclude by suggesting potential avenues for future research.