{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepchess-end-to-end-deep-neural-network-for","title":"DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess","arxiv_id":"1711.09667","date":"2017-11-27","proceeding":null,"authors":["Eli David","Nathan S. Netanyahu","Lior Wolf"],"abstract":"We present an end-to-end learning method for chess, relying on deep neural\nnetworks. Without any a priori knowledge, in particular without any knowledge\nregarding the rules of chess, a deep neural network is trained using a\ncombination of unsupervised pretraining and supervised training. The\nunsupervised training extracts high level features from a given position, and\nthe supervised training learns to compare two chess positions and select the\nmore favorable one. The training relies entirely on datasets of several million\nchess games, and no further domain specific knowledge is incorporated.\n  The experiments show that the resulting neural network (referred to as\nDeepChess) is on a par with state-of-the-art chess playing programs, which have\nbeen developed through many years of manual feature selection and tuning.\nDeepChess is the first end-to-end machine learning-based method that results in\na grandmaster-level chess playing performance.","url_abs":"http://arxiv.org/abs/1711.09667v1","url_pdf":"http://arxiv.org/pdf/1711.09667v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepchess-end-to-end-deep-neural-network-for","repo_url":"https://github.com/dangeng/DeepChess","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deepchess-end-to-end-deep-neural-network-for","repo_url":"https://github.com/ucdchessai/chess-ai","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"game-of-chess","task_name":"Game of Chess"},{"task_slug":null,"task_name":"Position"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.09667","atlas_url":"https://app.syntology.ai/?focus=1711.09667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.09667"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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