{"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/better-computer-go-player-with-neural-network","title":"Better Computer Go Player with Neural Network and Long-term Prediction","arxiv_id":"1511.06410","date":"2015-11-19","proceeding":null,"authors":["Yuandong Tian","Yan Zhu"],"abstract":"Competing with top human players in the ancient game of Go has been a\nlong-term goal of artificial intelligence. Go's high branching factor makes\ntraditional search techniques ineffective, even on leading-edge hardware, and\nGo's evaluation function could change drastically with one stone change. Recent\nworks [Maddison et al. (2015); Clark & Storkey (2015)] show that search is not\nstrictly necessary for machine Go players. A pure pattern-matching approach,\nbased on a Deep Convolutional Neural Network (DCNN) that predicts the next\nmove, can perform as well as Monte Carlo Tree Search (MCTS)-based open source\nGo engines such as Pachi [Baudis & Gailly (2012)] if its search budget is\nlimited. We extend this idea in our bot named darkforest, which relies on a\nDCNN designed for long-term predictions. Darkforest substantially improves the\nwin rate for pattern-matching approaches against MCTS-based approaches, even\nwith looser search budgets. Against human players, the newest versions,\ndarkfores2, achieve a stable 3d level on KGS Go Server as a ranked bot, a\nsubstantial improvement upon the estimated 4k-5k ranks for DCNN reported in\nClark & Storkey (2015) based on games against other machine players. Adding\nMCTS to darkfores2 creates a much stronger player named darkfmcts3: with 5000\nrollouts, it beats Pachi with 10k rollouts in all 250 games; with 75k rollouts\nit achieves a stable 5d level in KGS server, on par with state-of-the-art Go\nAIs (e.g., Zen, DolBaram, CrazyStone) except for AlphaGo [Silver et al.\n(2016)]; with 110k rollouts, it won the 3rd place in January KGS Go Tournament.","url_abs":"http://arxiv.org/abs/1511.06410v3","url_pdf":"http://arxiv.org/pdf/1511.06410v3.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":"better-computer-go-player-with-neural-network","repo_url":"https://github.com/facebookresearch/darkforestGo","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null},{"paper_slug":"better-computer-go-player-with-neural-network","repo_url":"https://github.com/CI-labo-OPU/FML_Competition2020","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"better-computer-go-player-with-neural-network","repo_url":"https://github.com/tianhai123/darkforestGo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"4k","task_name":"4k"},{"task_slug":"game-of-go","task_name":"Game of Go"}],"methods":[{"method_slug":"dcnn","method_name":"DCNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.06410","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}