{"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/online-double-oracle","title":"Online Double Oracle","arxiv_id":"2103.07780","date":"2021-03-13","proceeding":null,"authors":["Le Cong Dinh","Yaodong Yang","Stephen Mcaleer","Zheng Tian","Nicolas Perez Nieves","Oliver Slumbers","David Henry Mguni","Haitham Bou Ammar","Jun Wang"],"abstract":"Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence. This paper proposes new learning algorithms for solving two-player zero-sum normal-form games where the number of pure strategies is prohibitively large. Specifically, we combine no-regret analysis from online learning with Double Oracle (DO) methods from game theory. Our method -- \\emph{Online Double Oracle (ODO)} -- is provably convergent to a Nash equilibrium (NE). Most importantly, unlike normal DO methods, ODO is \\emph{rationale} in the sense that each agent in ODO can exploit strategic adversary with a regret bound of $\\mathcal{O}(\\sqrt{T k \\log(k)})$ where $k$ is not the total number of pure strategies, but rather the size of \\emph{effective strategy set} that is linearly dependent on the support size of the NE. On tens of different real-world games, ODO outperforms DO, PSRO methods, and no-regret algorithms such as Multiplicative Weight Update by a significant margin, both in terms of convergence rate to a NE and average payoff against strategic adversaries.","url_abs":"https://arxiv.org/abs/2103.07780v5","url_pdf":"https://arxiv.org/pdf/2103.07780v5.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":"online-double-oracle","repo_url":"https://github.com/npvoid/OnlineDoubleOracle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}