# Improving Multi-agent Coordination by Learning to Estimate Contention

arXiv: https://arxiv.org/abs/2105.04027 · HTML: https://mlsanity.com/paper/2105.04027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 47 references that are in the map.
- First posted on arXiv 9 May 2021.

## Builds on (most cited first, 2 of 2)

- [Putting Ridesharing to the Test: Efficient and Scalable Solutions and the Power of Dynamic Vehicle Relocation](https://mlsanity.com/paper/1912.08066/) (2019)
- [Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior](https://mlsanity.com/paper/1902.09359/) (2019)

## Abstract (arXiv)

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.