A distributed algorithm to obtain repeated games equilibria with discounting

DOI10.1016/j.amc.2019.124785zbMath1433.91008OpenAlexW2978682028WikidataQ127171534 ScholiaQ127171534MaRDI QIDQ2284815

Juan Parras, S. Zazo

Publication date: 15 January 2020

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2019.124785

zbMATH Keywords

Nash equilibriumrepeated gamescorrelated equilibriummultiagent learningfolk theoremaverage discounted payoff

Mathematics Subject Classification ID

Noncooperative games (91A10) Cooperative games (91A12)

Related Items (1)

Synchronous distributed ADMM for consensus convex optimization problems with self-loops

Uses Software

• Libratus
• AWESOME

Cites Work

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• Learning to compete, coordinate, and cooperate in repeated games using reinforcement learning
• Subjectivity and correlation in randomized strategies
• Multiagent learning using a variable learning rate
• Statistical physics of human cooperation
• AWESOME: a general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
• Simple Adaptive Strategies
• The Bargaining Problem
• The Communication Complexity of Atomic Commitment and of Gossiping
• On the Theory of Infinitely Repeated Games with Discounting
• Other Solutions to Nash's Bargaining Problem
• Proportional Solutions to Bargaining Situations: Interpersonal Utility Comparisons
• A Simple Adaptive Procedure Leading to Correlated Equilibrium
• Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
• Algorithmic Game Theory
• Equilibrium points in n -person games
• Stochastic Games

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