Neural Exploitation and Exploration of Contextual Bandits

• URL: http://arxiv.org/abs/2305.03784v1
• Date: Fri, 5 May 2023 18:34:49 GMT
• Title: Neural Exploitation and Exploration of Contextual Bandits
• Authors: Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He
• Abstract summary: We study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. EE-Net is a novel neural-based exploitation and exploration strategy. We show that EE-Net outperforms related linear and neural contextual bandit baselines on real-world datasets.
• Score: 51.25537742455235
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 bound for EE-Net and show that EE-Net outperforms related linear and neural contextual bandit baselines on real-world datasets.

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