Asymptotic Optimality for Decentralised Bandits
This work addresses efficient collaboration in distributed learning for applications like sensor networks, though it is incremental as it builds on prior Gossip-Insert-Eliminate methods.
The paper tackles the problem of minimizing regret for each agent in a decentralized multi-armed bandit setting with many agents and arms under communication constraints, presenting an algorithm that achieves asymptotic optimality with regret matching the full communication setting.
We consider a large number of agents collaborating on a multi-armed bandit problem with a large number of arms. The goal is to minimise the regret of each agent in a communication-constrained setting. We present a decentralised algorithm which builds upon and improves the Gossip-Insert-Eliminate method of Chawla et al. arxiv:2001.05452. We provide a theoretical analysis of the regret incurred which shows that our algorithm is asymptotically optimal. In fact, our regret guarantee matches the asymptotically optimal rate achievable in the full communication setting. Finally, we present empirical results which support our conclusions