Stochastic Linear Contextual Bandits with Diverse Contexts [article]

Weiqiang Wu, Jing Yang, Cong Shen
2020 arXiv   pre-print
In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits. As opposed to the previous view that contexts lead to more difficult bandit learning, we show that when the contexts are sufficiently diverse, the learner is able to utilize the information obtained during exploitation to shorten the exploration process, thus achieving reduced regret. We design the LinUCB-d algorithm, and propose a novel approach to analyze its regret performance. The main
more » ... retical result is that under the diverse context assumption, the cumulative expected regret of LinUCB-d is bounded by a constant. As a by-product, our results improve the previous understanding of LinUCB and strengthen its performance guarantee.
arXiv:2003.02681v1 fatcat:e6qz5bqbqzf4hgzfoq4cli6lke