Neural Contextual Bandits with UCB-based Exploration

TL;DR

This paper introduces NeuralUCB, a neural network-based algorithm for stochastic contextual bandits that achieves near-optimal regret bounds and demonstrates competitive empirical performance.

Contribution

It presents the first neural network-based contextual bandit algorithm with proven near-optimal regret guarantees.

Findings

NeuralUCB achieves  O(sqrt{T}) regret under standard assumptions.

NeuralUCB is empirically competitive with existing baselines.

The method leverages neural network representations for efficient exploration.

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.