Learning to Play Chess Using Temporal Differences

In this paper we present TDLEAF( ), a variation on the TD( ) algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in which our chess program "KnightCap" used TDLEAF( ) to learn its evaluation function while playing on Internet chess servers. The main success we report is that KnightCap improved from a 1650 rating to a 2150 rating in just 308 games and 3 days of play. As a reference, a rating of 1650 corresponds to about level B human play (on a scale from E (1000) to A (1800)), while 2150 is human master level. We discuss some of the reasons for this success, principle among them being the use of on-line, rather than self-play. We also investigate whether TDLEAF( ) can yield better results in the domain of backgammon, where TD( ) has previously yielded striking success.

• Type: Journal
• Year: 2000
• Where: ML
• Authors: Jonathan Baxter, Andrew Tridgell, Lex Weaver
• Keywords: Chess Program, Internet Chess Servers, KnightCap, Machine Learning, ML 2000