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Poster
Near-Optimal $\Phi$-Regret Learning in Extensive-Form Games
Ioannis Anagnostides · Gabriele Farina · Tuomas Sandholm

Thu Jul 27 01:30 PM -- 03:00 PM (PDT) @ Exhibit Hall 1 #740
in Poster Session 5 »
In this paper, we establish efficient and uncoupled learning dynamics so that, when employed by all players in multiplayer perfect-recall imperfect-information extensive-form games, the trigger regret of each player grows as $O(\log T)$ after $T$ repetitions of play. This improves exponentially over the prior best known trigger-regret bound of $O(T^{1/4})$, and settles a recent open question by Bai et al. (2022). As an immediate consequence, we guarantee convergence to the set of extensive-form correlated equilibria and coarse correlated equilibria at a near-optimal rate of $\frac{\log T}{T}$. Building on prior work, at the heart of our construction lies a more general result regarding fixed points deriving from rational functions with polynomial degree, a property that we establish for the fixed points of (coarse) trigger deviation functions. Moreover, our construction leverages a refined regret circuit for the convex hull, which---unlike prior guarantees---preserves the RVU property introduced by Syrgkanis et al. (NIPS, 2015); this observation has an independent interest in establishing near-optimal regret under learning dynamics based on a CFR-type decomposition of the regret.
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Author Information

Ioannis Anagnostides (Carnegie Mellon University)
Gabriele Farina (Meta AI)
Tuomas Sandholm (Carnegie Mellon University)

Tuomas Sandholm is Angel Jordan Professor of Computer Science at Carnegie Mellon University. He is Founder and Director of the Electronic Marketplaces Laboratory. He has published over 450 papers. With his student Vince Conitzer, he initiated the study of automated mechanism design in 2001. In parallel with his academic career, he was Founder, Chairman, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world's largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. He is Founder and CEO of Optimized Markets, Strategic Machine, and Strategy Robot. Also, his algorithms run the UNOS kidney exchange, which includes 69% of the transplant centers in the US. He has developed the leading algorithms for several general classes of game. The team that he leads is the two-time world champion in computer Heads-Up No-Limit Texas Hold’em poker, and Libratus became the first and only AI to beat top humans at that game. Among his many honors are the NSF Career Award, inaugural ACM Autonomous Agents Research Award, Sloan Fellowship, Carnegie Science Center Award for Excellence, Edelman Laureateship, Newell Award for Research Excellence, and Computers and Thought Award. He is Fellow of the ACM, AAAI, and INFORMS. He holds an honorary doctorate from the University of Zurich.

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