ICML 2022spotlight3 citations
Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
Nelson Vadori, Rahul Savani, Thomas Spooner, Sumitra Ganesh
Abstract
Cheung and Piliouras (2020) recently showed that two variants of the Multiplicative Weights Update method - OMWU and MWU - display opposite convergence properties depending on whether the game is zero-sum or cooperative. Inspired by this work and the recent literature on learning to optimize for single functions, we introduce a new framework for learning last-iterate convergence to Nash Equilibria in games, where the update rule’s coefficients (learning rates) along a trajectory are learnt by a reinforcement learning policy that is conditioned on the nature of the game:
BibTeX
@InProceedings{pmlr-v162-vadori22a,
title = {Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures},
author = {Vadori, Nelson and Savani, Rahul and Spooner, Thomas and Ganesh, Sumitra},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {21901--21926},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/vadori22a/vadori22a.pdf},
url = {https://proceedings.mlr.press/v162/vadori22a.html},
abstract = {Cheung and Piliouras (2020) recently showed that two variants of the Multiplicative Weights Update method - OMWU and MWU - display opposite convergence properties depending on whether the game is zero-sum or cooperative. Inspired by this work and the recent literature on learning to optimize for single functions, we introduce a new framework for learning last-iterate convergence to Nash Equilibria in games, where the update rule’s coefficients (learning rates) along a trajectory are learnt by a reinforcement learning policy that is conditioned on the nature of the game: