Hindsight and Sequential Rationality of Correlated Play

Authors

  • Dustin Morrill University of Alberta Alberta Machine Intelligence Institute
  • Ryan D'Orazio Université de Montréal Mila
  • Reca Sarfati Massachusetts Institute of Technology
  • Marc Lanctot DeepMind
  • James R Wright University of Alberta
  • Amy R Greenwald Brown University
  • Michael Bowling University of Alberta Alberta Machine Intelligence Institute DeepMind

Keywords:

Equilibrium, Imperfect Information, Multiagent Learning, Online Learning & Bandits

Abstract

Driven by recent successes in two-player, zero-sum game solving and playing, artificial intelligence work on games has increasingly focused on algorithms that produce equilibrium-based strategies. However, this approach has been less effective at producing competent players in general-sum games or those with more than two players than in two-player, zero-sum games. An appealing alternative is to consider adaptive algorithms that ensure strong performance in hindsight relative to what could have been achieved with modified behavior. This approach also leads to a game-theoretic analysis, but in the correlated play that arises from joint learning dynamics rather than factored agent behavior at equilibrium. We develop and advocate for this hindsight rationality framing of learning in general sequential decision-making settings. To this end, we re-examine mediated equilibrium and deviation types in extensive-form games, thereby gaining a more complete understanding and resolving past misconceptions. We present a set of examples illustrating the distinct strengths and weaknesses of each type of equilibrium in the literature, and prove that no tractable concept subsumes all others. This line of inquiry culminates in the definition of the deviation and equilibrium classes that correspond to algorithms in the counterfactual regret minimization (CFR) family, relating them to all others in the literature. Examining CFR in greater detail further leads to a new recursive definition of rationality in correlated play that extends sequential rationality in a way that naturally applies to hindsight evaluation.

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Published

2021-05-18

How to Cite

Morrill, D., D’Orazio, R., Sarfati, R., Lanctot, M., Wright, J. R., Greenwald, A. R., & Bowling, M. (2021). Hindsight and Sequential Rationality of Correlated Play. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 5584-5594. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16702

Issue

Section

AAAI Technical Track on Game Theory and Economic Paradigms