Neural Contextual Bandits with UCB-based Exploration

Dongruo Zhou; Lihong Li; Quanquan Gu

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arXiv · 1911.04462

Neural Contextual Bandits with UCB-based Exploration

Dongruo Zhou·Lihong Li·Quanquan Gu

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Abstract

We study the stochastic contextual bandit problem, where the reward is generated from an unknown function with additive noise. No assumption is made about the reward function other than boundedness. We propose a new algorithm, NeuralUCB, which leverages the representation power of deep neural networks and uses a neural network-based random feature mapping to construct an upper confidence bound (UCB) of reward for efficient exploration. We prove that, under standard assumptions, NeuralUCB achieves $\tilde O(\sqrt{T})$ regret, where $T$ is the number of rounds. To the best of our knowledge, it is the first neural network-based contextual bandit algorithm with a near-optimal regret guarantee. We also show the algorithm is empirically competitive against representative baselines in a number of benchmarks.

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BibTeX RIS

Dongruo Zhou, Lihong Li, Quanquan Gu. 2019-11-11. Neural Contextual Bandits with UCB-based Exploration. https://arxiv.org/abs/1911.04462

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