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  Temporal Difference Learning of Position Evaluation in the Game of Go

Schraudolph, N., Dayan, P., & Sejnowski, T. (1994). Temporal Difference Learning of Position Evaluation in the Game of Go. In J. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in Neural Information Processing Systems 6 (pp. 817-824). San Mateo, CA, USA: Morgan Kaufmann.

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 Creators:
Schraudolph, NN, Author
Dayan, P1, Author              
Sejnowski, TJ, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: The game of Go has a high branching factor that defeats the tree search approach used in computer chess, and long-range spatiotemporal 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 selfplay 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.

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 Dates: 1994
 Publication Status: Published in print
 Pages: -
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Title: Seventh Annual Conference on Neural Information Processing Systems (NIPS 1993)
Place of Event: Denver, CO, USA
Start-/End Date: 1993-11-29 - 1993-12-02

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Title: Advances in Neural Information Processing Systems 6
Source Genre: Proceedings
 Creator(s):
Cowan, JD, Editor
Tesauro, G, Editor
Alspector, J, Editor
Affiliations:
-
Publ. Info: San Mateo, CA, USA : Morgan Kaufmann
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 817 - 824 Identifier: ISBN: 1-55860-322-0