{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T05:33:01Z","timestamp":1769059981614,"version":"3.49.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>\n      \n        We propose a novel online learning method for minimizing regret in large extensive-form games. The approach learns a function approximator online to estimate the regret for choosing a particular action.  A no-regret algorithm uses these estimates in place of the true regrets to define a sequence of policies.  We prove the approach sound by providing a bound relating the quality of the function approximation and regret of the algorithm.  A corollary being that the method is guaranteed to converge to a Nash equilibrium in self-play so long as the regrets are ultimately realizable by the function approximator.  Our technique can be understood as a principled generalization of existing work onabstraction in large games; in our work, both the abstraction as well as the equilibrium are learned during self-play.  We demonstrate empirically the method achieves higher quality strategies than state-of-the-art abstraction techniques given the same resources.\n      \n    <\/jats:p>","DOI":"10.1609\/aaai.v29i1.9445","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T19:08:52Z","timestamp":1656011332000},"source":"Crossref","is-referenced-by-count":11,"title":["Solving Games with Functional Regret Estimation"],"prefix":"10.1609","volume":"29","author":[{"given":"Kevin","family":"Waugh","sequence":"first","affiliation":[]},{"given":"Dustin","family":"Morrill","sequence":"additional","affiliation":[]},{"given":"James","family":"Bagnell","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Bowling","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2015,2,18]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/9445\/9304","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/9445\/9304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T19:08:52Z","timestamp":1656011332000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/9445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,2,18]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2015,3,1]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v29i1.9445","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2015,2,18]]}}}