Sub-sampling for Efficient Non-Parametric Bandit Exploration
This addresses the need for flexible and robust exploration algorithms in bandit models, offering a novel method that is incremental in combining existing sub-sampling ideas.
The paper tackles the problem of achieving asymptotically optimal regret in multi-armed bandits across different arm distributions without distribution-dependent tuning, proposing RB-SDA, a re-sampling-based algorithm that matches Thompson Sampling's performance without prior specification.
In this paper we propose the first multi-armed bandit algorithm based on re-sampling that achieves asymptotically optimal regret simultaneously for different families of arms (namely Bernoulli, Gaussian and Poisson distributions). Unlike Thompson Sampling which requires to specify a different prior to be optimal in each case, our proposal RB-SDA does not need any distribution-dependent tuning. RB-SDA belongs to the family of Sub-sampling Duelling Algorithms (SDA) which combines the sub-sampling idea first used by the BESA [1] and SSMC [2] algorithms with different sub-sampling schemes. In particular, RB-SDA uses Random Block sampling. We perform an experimental study assessing the flexibility and robustness of this promising novel approach for exploration in bandit models.