2021/03/30 by Puning Zhao, Zhao, Puning, Lifeng Lai +1
Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Bandit Algorithms Research #Computer science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical economics #Mathematical optimization #Mathematics #Reinforcement Learning in Robotics #Smart Grid Energy Management #Stochastic optimization #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2103.16082
arxiv created 2021/03/30 · openalex publication_date 2021/03/30 · arxiv updated 2021/03/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we analyze the continuous armed bandit problems for nonconvex cost functions under certain smoothness and sublevel set assumptions. We first derive an upper bound on the expected cumulative regret of a simple bin splitting method. We then propose an adaptive bin splitting method, which can significantly improve the performance. Furthermore, a minimax lower bound is derived, which shows that our new adaptive method achieves locally minimax optimal expected cumulative regret.