{"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/approximating-poker-probabilities-with-deep","title":"Approximating Poker Probabilities with Deep Learning","arxiv_id":"1808.07220","date":"2018-08-22","proceeding":null,"authors":["Brandon Da Silva"],"abstract":"Many poker systems, whether created with heuristics or machine learning, rely\non the probability of winning as a key input. However calculating the precise\nprobability using combinatorics is an intractable problem, so instead we\napproximate it. Monte Carlo simulation is an effective technique that can be\nused to approximate the probability that a player will win and/or tie a hand.\nHowever, without the use of a memory-intensive lookup table or a supercomputer,\nit becomes infeasible to run millions of times when training an agent with\nself-play. To combat the space-time tradeoff, we use deep learning to\napproximate the probabilities obtained from the Monte Carlo simulation with\nhigh accuracy. The learned model proves to be a lightweight alternative to\nMonte Carlo simulation, which ultimately allows us to use the probabilities as\ninputs during self-play efficiently. The source code and optimized neural\nnetwork can be found at\nhttps://github.com/brandinho/Poker-Probability-Approximation","url_abs":"http://arxiv.org/abs/1808.07220v2","url_pdf":"http://arxiv.org/pdf/1808.07220v2.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":"approximating-poker-probabilities-with-deep","repo_url":"https://github.com/brandinho/Poker-Probability-Approximation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"card-games","task_name":"Card Games"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"game-of-poker","task_name":"Game of Poker"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}