{"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/dota-2-with-large-scale-deep-reinforcement","title":"Dota 2 with Large Scale Deep Reinforcement Learning","arxiv_id":"1912.06680","date":"2019-12-13","proceeding":null,"authors":["Christopher Berner","Greg Brockman","Brooke Chan","Vicki Cheung","Przemysław Dębiak","Christy Dennison","David Farhi","Quirin Fischer","Shariq Hashme","Chris Hesse","Rafal Józefowicz","Scott Gray","Catherine Olsson","Jakub Pachocki","Michael Petrov","Henrique Pondé de Oliveira Pinto","Jonathan Raiman","Tim Salimans","Jeremy Schlatter","Jonas Schneider","Szymon Sidor","Ilya Sutskever","Jie Tang","Filip Wolski","Susan Zhang"],"abstract":"On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. 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By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.","url_abs":"https://arxiv.org/abs/1912.06680v1","url_pdf":"https://arxiv.org/pdf/1912.06680v1.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":"dota-2-with-large-scale-deep-reinforcement","repo_url":"https://github.com/bilibili/LastOrder-Dota2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"dota-2","task_name":"Dota 2"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1912.06680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.06680"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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