TY - JOUR
T1 - Asymptotic Optimality for Decentralised Bandits
AU - Newton, Conor J.
AU - Ganesh, Ayalvadi
AU - Reeve, Henry W.J.
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/6/20
Y1 - 2022/6/20
N2 - 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. (International conference on artificial intelligence and statistics, pp 3471–3481, 2020). 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.
AB - 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. (International conference on artificial intelligence and statistics, pp 3471–3481, 2020). 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.
UR - http://www.scopus.com/inward/record.url?scp=85132735272&partnerID=8YFLogxK
U2 - 10.1007/s13235-022-00451-1
DO - 10.1007/s13235-022-00451-1
M3 - Article (Academic Journal)
AN - SCOPUS:85132735272
SN - 2153-0785
JO - Dynamic Games and Applications
JF - Dynamic Games and Applications
ER -