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We consider an online decision making setting known as contextual banditproblem, and propose an approach for improving contextual bandit performance byusing an adaptive feature extraction (representation learning) based on onlineclustering. Our approach starts with an off-line pre-training on unlabeledhistory of contexts (which can be exploited by our approach, but not by thestandard contextual bandit), followed by an online selection and adaptation ofencoders. Specifically, given an input sample (context), the proposed approachselects the most appropriate encoding function to extract a feature vectorwhich becomes an input for a contextual bandit, and updates both the bandit andthe encoding function based on the context and on the feedback (reward). Ourexperiments on a variety of datasets, and both in stationary and non-stationaryenvironments of several kinds demonstrate clear advantages of the proposedadaptive representation learning over the standard contextual bandit based on"raw" input contexts.

Contextual Bandit with Adaptive Feature Extraction