Parallel Best Arm Identification in Heterogeneous Environments
Proceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures(2022)
摘要
In this paper, we study the tradeoffs between the time and the number of
communication rounds of the best arm identification problem in the
heterogeneous collaborative learning model, where multiple agents interact with
possibly different environments and they want to learn in parallel an objective
function in the aggregated environment. By proving almost tight upper and lower
bounds, we show that collaborative learning in the heterogeneous setting is
inherently more difficult than that in the homogeneous setting in terms of the
time-round tradeoff.
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