2021/08/09 by Keqin Liu, Liu, Keqin, Richard W. Weber +3
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Age of Information Optimization #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.2108.03812
openalex publication_date 2021/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider a class of restless bandit problems that finds a broad application area in reinforcement learning and stochastic optimization. We consider N independent discrete-time Markov processes, each of which had two possible states: 1 and 0 (`good' and `bad'). Only if a process is both in state 1 and observed to be so does reward accrue. The aim is to maximize the expected discounted sum of returns over the infinite horizon subject to a constraint that only M (