From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Abstract
In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG). We generalize existing results of Poincar{é} recurrence from normal-form games to zero-sum two-player imperfect information games and other sequential game settings. We then investigate how adapting the reward (by adding a regularization term) of the game can give strong convergence guarantees in monotone games. We continue by showing how this reward adaptation technique can be leveraged to build algorithms that converge exactly to the Nash equilibrium. Finally, we show how these insights can be directly used to build state-of-the-art model-free algorithms for zero-sum two-player Imperfect Information Games (IIG).
Cite
Text
Perolat et al. "From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization." International Conference on Machine Learning, 2021.Markdown
[Perolat et al. "From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/perolat2021icml-poincare/)BibTeX
@inproceedings{perolat2021icml-poincare,
title = {{From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization}},
author = {Perolat, Julien and Munos, Remi and Lespiau, Jean-Baptiste and Omidshafiei, Shayegan and Rowland, Mark and Ortega, Pedro and Burch, Neil and Anthony, Thomas and Balduzzi, David and De Vylder, Bart and Piliouras, Georgios and Lanctot, Marc and Tuyls, Karl},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {8525-8535},
volume = {139},
url = {https://mlanthology.org/icml/2021/perolat2021icml-poincare/}
}