An Introduction to Counterfactual Regret Minimization
In 2000, Hart and Mas-Colell introduced the important game-theoretic algorithm of regret matching. Players reach equilibrium play by tracking regrets for past plays, making future plays proportional to positive regrets. The technique is not only simple and intuitive; it has sparked a revolution in computer game play of some of the most difficult bluffing games, including clear domination of annual computer poker competitions.
Since the algorithm is relatively recent, there are few curricular materials available to introduce regret-based algorithms to the next generation of researchers and practitioners in this area. These materials represent a modest first step towards making recent innovations more accessible to advanced Computer Science undergraduates, graduate students, interested researchers, and ambitious practitioners.
In Section 2, we introduce the concept of player regret, describe the regret-matching algorithm, present a rock-paper-scissors worked example in the literate programming style, and suggest related exercises. Counterfactual Regret Minimization (CFR) is introduced in Section 3 with a worked example solving Kuhn Poker. Supporting code is provided for a substantive CFR exercise computing optimal play for 1-die-versus-1-die Dudo. In Section 4, we briey mention means of \cleaning" approximately optimal computed policies, which can in many cases improve results. Section 5 covers an advanced application of CFR to games with repeated states (e.g. through imperfect recall abstraction) that can reduce computational complexity of a CFR training iteration from exponential to linear. Here, we use our independently devised game of Liar Die to demonstrate application of the algorithm. We then suggest that the reader apply the technique to 1-die-versus-1-die Dudo with a memory of 3 claims. In Section 6, we briefly discuss an open research problem: Among possible equilibrium strategies, how do we compute one that optimally exploits opponent errors? The reader is invited to modify our Liar Die example code to so as to gain insight to this interesting problem. Finally, in Section 7, we suggest further challenge problems and paths for continued learning. [excerpt]
Neller, Todd W., and Marc Lanctot. “An Introduction to Counterfactual Regret Minimization.” Gettysburg: Association for the Advancement of Artificial Intelligence, 2013.