Online Learning of Rested and Restless Bandits

Abstract

In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K finite-state discrete-time Markov chains (arms) with unknown state spaces and statistics. At each time step the player can play M arms. The objective of the user is to decide for each step which M of the K arms to play over a sequence of trials so as to maximize its long term reward. The restless multiarmed bandit is particularly relevant to the application of opportunistic spectrum access (OSA), where a (secondary) user has access to a set of K channels, each of time-varying condition as a result of random fading and/or certain primary users' activities.

References in corpus (2)

• Distributed Learning in Multi-Armed Bandit with Multiple Players
• Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret

Cited by in corpus (7)

• Distributed Online Learning in Social Recommender Systems
• Game of Thrones: Fully Distributed Learning for Multi-Player Bandits
• Markovian restless bandits and index policies: A review
• Action-Manipulation Attacks Against Stochastic Bandits: Attacks and Defense
• A Learning-Based Two-Stage Spectrum Sharing Strategy with Multiple Primary Transmit Power Levels
• Learning in Restless Bandits under Exogenous Global Markov Process
• Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks