Neural Contextual Bandits Under Delayed Feedback Constraints

Abstract

This paper presents a new algorithm for neural contextual bandits (CBs) that addresses the challenge of delayed reward feedback, where the reward for a chosen action is revealed after a random, unknown delay. This scenario is common in applications such as online recommendation systems and clinical trials, where reward feedback is delayed because the outcomes or results of a user's actions (such as recommendations or treatment responses) take time to manifest and be measured. The proposed algorithm, called Delayed NeuralUCB, uses an upper confidence bound (UCB)-based exploration strategy. Under the assumption of independent and identically distributed sub-exponential reward delays, we derive an upper bound on the cumulative regret over a T-length horizon. We further consider a variant of the algorithm, called Delayed NeuralTS, that uses Thompson Sampling-based exploration. Numerical experiments on real-world datasets, such as MNIST and Mushroom, along with comparisons to benchmark approaches, demonstrate that the proposed algorithms effectively manage varying delays and are well-suited for complex real-world scenarios.