Papers › Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
MohammadJavad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh
We study a sequential decision problem where the learner faces a sequence of K-armed bandit tasks. The task boundaries might be known (the bandit meta-learning setting), or unknown (the non-stationary bandit setting). For a given integer M≤K, the learner aims to compete with the best subset of arms of size M. We design an algorithm based on a reduction to bandit submodular maximization, and show that, for T rounds comprised of N tasks, in the regime of large number of tasks and small number of optimal arms M, its regret in both settings is smaller than the simple baseline of Õ(√(KNT)) that can be obtained by using standard algorithms designed for non-stationary bandit problems. For the bandit meta-learning problem with fixed task length τ, we show that the regret of the algorithm is bounded as Õ(NM√(M τ)+N^(2/3)Mτ). Under additional assumptions on the identifiability of the optimal arms in each task, we show a bandit meta-learning algorithm with an improved Õ(N√(M τ)+N^(1/2)√(M K τ)) regret.
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