Publication

Improving Multi-agent Coordination by Learning to Estimate Contention

Boi Faltings, Panayiotis Danassis
2021
Reports

Abstract

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need for mutually consistent actions) by relying on the ALMA heuristic as a coordination mechanism for each stage game. ALMA-Learning is decentralized, observes only own action/reward pairs, requires no inter-agent communication, and achieves near-optimal (

Official source

https://infoscience.epfl.ch/entities/publication/de2c5f70-6cb0-40b0-8d7e-0b77031d2b57

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Improving Multi-agent Coordination by Learning to Estimate Contention

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