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Learning to Evaluate Go Positions via Temporal Difference Methods

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Citation

Schraudolph, N., Dayan, P., & Sejnowski, T. (2001). Learning to Evaluate Go Positions via Temporal Difference Methods. In N. Baba, & L. Jain (Eds.), Computational Intelligence in Games (pp. 77-98). Heidelberg, Germany: Physica Verlag.


Cite as: http://hdl.handle.net/21.11116/0000-0007-5691-4
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 neural networks to evaluate Go positions via temporal difference (TD) learning.