Improving Multi-agent Coordination by Learning to Estimate Contention

• URL: http://arxiv.org/abs/2105.04027v1
• Date: Sun, 9 May 2021 21:30:48 GMT
• Title: Improving Multi-agent Coordination by Learning to Estimate Contention
• Authors: Panayiotis Danassis, Florian Wiedemair, Boi Faltings
• Abstract summary: We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. ALMA-Learning is decentralized, observes only own action/reward pairs, requires no inter-agent communication, and achieves near-optimal (5% loss) and fair coordination.
• Score: 24.52552750240412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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