---
title: "Improving Multi-agent Coordination by Learning to Estimate Contention"
authors:
  - "Panayiotis Danassis"
  - "Florian Wiedemair"
  - "Boi Faltings"
doi: "10.24963/ijcai.2021/18"
url: "https://ijcai.org/proceedings/2021/18"
pdf: "https://ijcai.org/proceedings/2021/0018.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "125-131"
track: "Main Track"
keywords:
  - "Agent-based and Multi-agent Systems: Resource Allocation"
  - "Agent-based and Multi-agent Systems: Coordination and Cooperation"
  - "Agent-based and Multi-agent Systems: Multi-agent Learning"
---

# Improving Multi-agent Coordination by Learning to Estimate Contention

Panayiotis Danassis, Florian Wiedemair, Boi Faltings

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 125-131. DOI: [10.24963/ijcai.2021/18](https://doi.org/10.24963/ijcai.2021/18)

## 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 (<5% loss) and fair coordination in a variety of synthetic scenarios and a real-world meeting scheduling problem. The lightweight nature and fast learning constitute ALMA-Learning ideal for on-device deployment.