  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.
