Deep Counterfactual Regret Minimization

Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:793-802, 2019.

Abstract

Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game. This process can be problematic because aspects of abstraction are often manual and domain specific, abstraction algorithms may miss important strategic nuances of the game, and there is a chicken-and-egg problem because determining a good abstraction requires knowledge of the equilibrium of the game. This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-brown19b, title = {Deep Counterfactual Regret Minimization}, author = {Brown, Noam and Lerer, Adam and Gross, Sam and Sandholm, Tuomas}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {793--802}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/brown19b/brown19b.pdf}, url = {https://proceedings.mlr.press/v97/brown19b.html}, abstract = {Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game. This process can be problematic because aspects of abstraction are often manual and domain specific, abstraction algorithms may miss important strategic nuances of the game, and there is a chicken-and-egg problem because determining a good abstraction requires knowledge of the equilibrium of the game. This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.} }
Endnote
%0 Conference Paper %T Deep Counterfactual Regret Minimization %A Noam Brown %A Adam Lerer %A Sam Gross %A Tuomas Sandholm %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-brown19b %I PMLR %P 793--802 %U https://proceedings.mlr.press/v97/brown19b.html %V 97 %X Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game. This process can be problematic because aspects of abstraction are often manual and domain specific, abstraction algorithms may miss important strategic nuances of the game, and there is a chicken-and-egg problem because determining a good abstraction requires knowledge of the equilibrium of the game. This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.
APA
Brown, N., Lerer, A., Gross, S. & Sandholm, T.. (2019). Deep Counterfactual Regret Minimization. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:793-802 Available from https://proceedings.mlr.press/v97/brown19b.html.

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