Zeta Chess

Neural Networks on GPU

Currently there is much going on with neural networks for chess. With GiraffeAlphaZero, and its open source adaptation LC0 (Leela Chess Zero), it was shown that, with enough horse power, artificial neural networks are competitive in computer chess.

Currently LC0 uses an MCTS, Monte-Carlo Tree Search, approach with GPU as neural network accelerator for position evaluation.

My own experiments showed that AlphaBeta search is superior to MCTS, but current GPU architectures suffer from host-device latency, so you have to couple tasks to batches to be executed in one run on the GPU, not that conform with the serial nature of AlphaBeta.

With upcoming GPGPU architectures (or ANN accelerators) with less latency there might be AlphaBeta ANN engines possible...

Google's AlphaGo Deepmind and Chess Giraffe

It was in the news, Google's AlphaGo won against the European Champion Fan Hui in the game of GO...another frontier is fallen to computer domination.

The question if such an attempt with deep neural networks works also for chess was answered by Matthew Lai in his Master Thesis with his chess engine Giraffe, which reached the level of an FIDE International Master (about 2400 Elo), an astounding achievement considering only 4 month of work....

...so, when are we going to see AlphaChess Mr. Lai? :-)

Links:

Giraffe: Using Deep Reinforcement Learning to Play Chess by Matthew Lai, 2015

Mastering the Game of Go with Deep Neural Networks and Tree Search by Google Deepmind, 2016

Learning to Play the Game of Chess by Sebastian Thrun, 1995

NeuroChess by Sebastian Thrun on CPW

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