language: en

Temporal Difference Learning of Position Evaluation in the Game of Go

Nicol N. Schraudolph Peter Dayan Terrence J. Sejnowski
schraudo@salk.edu dayan@salk.edu terry@salk.edu
Computational Neurobiology Laboratory The Salk Institute for Biological Studies
San Diego, CA 92186-5800

Abstract The game of Go has a high branching factor that defeats the tree search approach used in computer chess, and long-range spa-tiotemporal interactions that make position evaluation extremely difficult. Development of conventional Go programs is hampered by their knowledge-intensive nature. We demonstrate a viable alternative by training networks to evaluate Go positions via temporal difference (TD) learning. Our approach is based on network architectures that reflect the spatial organization of both input and reinforcement signals on the Go board, and training protocols that provide exposure to competent (though unlabelled) play. These techniques yield far better performance than undifferentiated networks trained by self-play alone. A network with less than 500 weights learned within 3,000 games of 9x9 Go a position evaluation function that enables a primitive one-ply search to defeat a commercial Go program at a low playing level.

1 INTRODUCTION