[2305.03784] Neural Exploitation and Exploration of Contextual Bandits

Computer Science > Machine Learning arXiv:2305.03784 (cs) [Submitted on 5 May 2023 (v1), last revised 5 Apr 2026 (this version, v3)] Title:Neural Exploitation and Exploration of Contextual Bandits Authors:Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He View a PDF of the paper titled Neural Exploitation and Exploration of Contextual Bandits, by Yikun Ban and 3 other authors View PDF HTML (experimental) Abstract:In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration trade-off in bandits, there are three main techniques: epsilon-greedy, Thompson Sampling (TS), and Upper Confidence Bound (UCB). In recent literature, a series of neural bandit algorithms have been proposed to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration. In this paper, instead of calculating a large-deviation based statistical bound for exploration like previous methods, we propose, ``EE-Net,'' a novel neural-based exploitation and exploration strategy. In addition to using a neural network (Exploitation network) to learn the reward function, EE-Net uses another neural network (Exploration network) to adaptively learn the potential gains compared to the currently estimated reward for exploration. We provide an instance-based $\widetilde{\mathcal{O}}(\sqrt{T})$ regret upper...