Neural Contextual Bandits with Deep Representation and Shallow ExplorationDownload PDF

Sep 29, 2021 (edited Mar 16, 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: neural network, deep representation learning
  • Abstract: We study neural contextual bandits, a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the specific reward generating function is unknown. We propose a novel learning algorithm that transforms the raw feature vector using the last hidden layer of a deep ReLU neural network (deep representation learning), and uses an upper confidence bound (UCB) approach to explore in the last linear layer (shallow exploration). We prove that under standard assumptions, our proposed algorithm achieves $\tilde{O}(\sqrt{T})$ finite-time regret, where $T$ is the learning time horizon. Compared with existing neural contextual bandit algorithms, our approach is computationally much more efficient since it only needs to explore in the last layer of the deep neural network.
  • One-sentence Summary: A new neural network based algorithm for contextual bandit problems with theoretical guarantees and empirical advantages.
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