DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

Conference paper

DOI: 10.1007/978-3-319-44781-0_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9887)
Cite this paper as:
David O.E., Netanyahu N.S., Wolf L. (2016) DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess. In: Villa A., Masulli P., Pons Rivero A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science, vol 9887. Springer, Cham


We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the more favorable one. The training relies entirely on datasets of several million chess games, and no further domain specific knowledge is incorporated.

The experiments show that the resulting neural network (referred to as DeepChess) is on a par with state-of-the-art chess playing programs, which have been developed through many years of manual feature selection and tuning. DeepChess is the first end-to-end machine learning-based method that results in a grandmaster-level chess playing performance.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Omid E. David
    • 1
    • 2
  • Nathan S. Netanyahu
    • 2
    • 3
  • Lior Wolf
    • 1
  1. 1.The Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.Department of Computer ScienceBar-Ilan UniversityRamat-ganIsrael
  3. 3.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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