UAI 2021poster0 citations
Learning in Multi-Player Stochastic Games
Abstract
We consider the problem of simultaneous learning in stochastic games with many players in the finite-horizon setting. While the typical target solution for a stochastic game is a Nash equilibrium, this is intractable with many players. We instead focus on variants of
BibTeX
@InProceedings{pmlr-v161-brown21a,
title = {Learning in Multi-Player Stochastic Games},
author = {Brown, William},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {1927--1937},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/brown21a/brown21a.pdf},
url = {https://proceedings.mlr.press/v161/brown21a.html},
abstract = {We consider the problem of simultaneous learning in stochastic games with many players in the finite-horizon setting. While the typical target solution for a stochastic game is a Nash equilibrium, this is intractable with many players. We instead focus on variants of