Learning not to regret

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00378617" target="_blank" >RIV/68407700:21230/24:00378617 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/24:10490822

Result on the web

<a href="https://doi.org/10.1609/aaai.v38i14.29443" target="_blank" >https://doi.org/10.1609/aaai.v38i14.29443</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/aaai.v38i14.29443" target="_blank" >10.1609/aaai.v38i14.29443</a>

Result language

angličtina

Original language name

Learning not to regret

Original language description

The literature on game-theoretic equilibrium finding predominantly focuses on single games or their repeated play. Nevertheless, numerous real-world scenarios feature playing a game sampled from a distribution of similar, but not identical games, such as playing poker with different public cards or trading correlated assets on the stock market. As these similar games feature similar equilibra, we investigate a way to accelerate equilibrium finding on such a distribution. We present a novel "learning not to regret" framework, enabling us to meta-learn a regret minimizer tailored to a specific distribution. Our key contribution, Neural Predictive Regret Matching, is uniquely meta-learned to converge rapidly for the chosen distribution of games, while having regret minimization guarantees on any game. We validated our algorithms' faster convergence on a distribution of river poker games. Our experiments show that the meta-learned algorithms outpace their non-meta-learned counterparts, achieving more than tenfold improvements.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA22-26655S" target="_blank" >GA22-26655S: Algorithms for Playing Massive Imperfect-Information Games</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 38th AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-887-9

ISSN

2159-5399

e-ISSN

2374-3468

Number of pages

9

Pages from-to

15202-15210

Publisher name

AAAI Press

Place of publication

Menlo Park

Event location

Vancouver

Event date

Feb 20, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001239983500003