  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.
