Reinforcement Learning for Dynamic Dimensioning of Cloud Caches: A Restless Bandit Approach-Reference-Cited by-同舟云学术

Reinforcement Learning for Dynamic Dimensioning of Cloud Caches: A Restless Bandit Approach

Author:

Xiong Guojun¹,Wang Shufan¹,Yan Gang¹,Li Jian¹

Affiliation:

1. Suny-Binghamton University

Funder

U.S. Department of Energy

Publisher

IEEE

Link

Reference55 articles.

2. Reinforcement Learning Augmented Asymptotically Optimal Index Policies for Finite-Horizon Restless Bandits;xiong;Proc Of AAAI,2022

3. Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits;wang;Proc of NeurIPS,2020

4. Thompson Sampling in Non-Episodic Restless Bandits;jung;arXiv preprint arXiv 1910 05656,2019

5. Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems;jung;Proc of NeurIPS,2019

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

3. Caching Dynamic Contents via Mortal Restless Bandits;2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt);2023-08-24

5. Index-aware reinforcement learning for adaptive video streaming at the wireless edge;Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing;2022-10-03