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Fast Seed-Learning Algorithms for Games

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Computers and Games (CG 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10068))

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Abstract

Recently, a methodology has been proposed for boosting the computational intelligence of randomized game-playing programs. We propose faster variants of these algorithms, namely rectangular algorithms (fully parallel) and bandit algorithms (faster in a sequential setup). We check the performance on several board games and card games. In addition, in the case of Go, we check the methodology when the opponent is completely distinct to the one used in the training.

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Notes

  1. 1.

    GnuGo does not accept MCTS for 19\(\,\times \,\)19.

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Correspondence to Jialin Liu .

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Liu, J., Teytaud, O., Cazenave, T. (2016). Fast Seed-Learning Algorithms for Games. In: Plaat, A., Kosters, W., van den Herik, J. (eds) Computers and Games. CG 2016. Lecture Notes in Computer Science(), vol 10068. Springer, Cham. https://doi.org/10.1007/978-3-319-50935-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-50935-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50934-1

  • Online ISBN: 978-3-319-50935-8

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