A potentialization algorithm for games with applications to multi-agent learning in repeated games

Philipp Lakheshar, Sharwin Rezagholi
2/21/2026
cs.MAcs.GT

No confident code implementation found for this paper. 3 possible matches can be shown below.

Abstract

We investigate an algorithm that assigns to any game in normal form an approximating game that admits an ordinal potential function. Due to the properties of potential games, the algorithm equips every game with a surrogate reward structure that allows efficient multi-agent learning. Numerical simulations using the replicator dynamics show that 'potentialization' guarantees convergence to stable agent behavior.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics

86% match2024

Mutation-Bias Learning in Games

86% match2024

Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games

85% match2026

Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics

84% match2023

A Data Driven Structural Decomposition of Dynamic Games via Best Response Maps

84% match2026

Maximizing utility in multi-agent environments by anticipating the behavior of other learners

84% match2024

Cite this paper

@article{lakheshar2026potentialization,
  title  = {A potentialization algorithm for games with applications to multi-agent learning in repeated games},
  author = {Philipp Lakheshar and Sharwin Rezagholi},
  year   = {2026},
  eprint = {2602.18925},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.18925v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2602.18925.svg)](https://codeofpaper.com/paper/2602.18925)

Discussion