Proceedings

Solving Heads-Up Limit Texas Hold'em

Download BibTeX Cite

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 645-651.

Abstract

Cepheus is the first computer program to essentially solve a game of imperfect information that is played competitively by humans. The game it plays is heads-up limit Texas hold'em poker, a game with over 10^14 information sets, and a challenge problem for artificial intelligence for over 10 years. Cepheus was trained using a new variant of Counterfactual Regret Minimization (CFR), called CFR+, using 4800 CPUs running for 68 days. In this paper we describe in detail the engineering details required to make this computation a reality. We also prove the theoretical soundness of CFR+ and its component algorithm, regret-matching+. We further give a hint towards understanding the success of CFR+ by proving a tracking regret bound for this new regret matching algorithm. We present results showing the role of the algorithmic components and the engineering choices to the success of CFR+.

Oskari Tammelin, Neil Burch, Michael Johanson, and Michael H. Bowling. 2015. Solving Heads-Up Limit Texas Hold'em. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 645–651. https://ijcai.org/proceedings/2015/97
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards