Poster: Multi-agent Combinatorial Bandits with Moving Arms

Abstract

In this paper, we study a distributed stochastic multi-armed bandit problem that can address many real-world problems such as task assignment for multiple crowdsourcing platforms, traffic scheduling in wireless networks with multiple access points and caching at cellular network edge. We propose an efficient algorithm called multi-agent combinatorial upper confidence bound (MACUCB) with provable performance guarantees and low communication overhead. Furthermore, we perform extensive experiments to show the effectiveness of the proposed algorithm.