Neural Exploitation and Exploration of Contextual Bandits

Дата публикации: 17-08-2026 20:26:00

In this paper, we study the neural exploration strategy for contextual bandits.
The dilemma of exploitation and exploration widely exists in real-world applications such as recommender systems, online advertising, and clinical trials.
Contextual bandits provide principled methods to solve this dilemma, including two prevalent techniques: Thompson Sampling (TS), and Upper Confidence Bound (UCB).
Neural contextual bandits have been studied to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration.
In this paper, we introduce, EE-Net, which is a novel framework to utilize another neural network to learn the potential gain of exploitation neural network for exploration, different from UCB-based and TS-based approaches that rely on the large-deviation-based statistical confidence bound. In addition, we provide an instance-based $\widetilde{\mathcal{O}}(\sqrt{T})$ regret upper bound for EE-Net with a new proof workflow. Empirically, we show that EE-Net outperforms related linear and neural contextual bandit baselines on real-world datasets.

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