Results 1 to 10 of about 128 (120)

Markovian Restless Bandits and Index Policies: A Review

Mathematics, 2023
The restless multi-armed bandit problem is a paradigmatic modeling framework for optimal dynamic priority allocation in stochastic models of wide-ranging applications that has been widely investigated and applied since its inception in a seminal paper by
JOSÉ Niño-Mora
exaly   +6 more sources

Non-Myopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks [PDF]

Sensors
This paper addresses beam scheduling for tracking multiple smart targets in phased array radar networks, aiming to mitigate the performance degradation in previous myopic scheduling methods and enhance the tracking performance, which is measured by a ...
Yuhang Hao, Zengfu Wang, José Niño-Mora, Jing Fu, Quan Pan, Min Yang   +5 more
doaj   +2 more sources

A Fast-Pivoting Algorithm for Whittle’s Restless Bandit Index [PDF]

Mathematics, 2020
The Whittle index for restless bandits (two-action semi-Markov decision processes) provides an intuitively appealing optimal policy for controlling a single generic project that can be active (engaged) or passive (rested) at each decision epoch, and ...
José Niño-Mora
doaj   +4 more sources

Rested and Restless Bandits With Constrained Arms and Hidden States: Applications in Social Networks and 5G Networks

IEEE Access, 2018
The problem of rested and restless multi-armed bandits with constrained availability (RMAB-CA) of arms is considered. The states of arms evolve in Markovian manner and the exact states are hidden from the decision maker. First, some structural results on
Varun Mehta, Rahul Meshram, Kesav Kaza   +2 more
exaly   +3 more sources

Online Learning of Rested and Restless Bandits [PDF]

IEEE Transactions on Information Theory, 2012
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
Cem Tekin, Mingyan Liu
exaly   +3 more sources

Asymptotic Optimal Control of Markov-Modulated Restless Bandits [PDF]

Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2018
This paper studies optimal control subject to changing conditions. This is an area that recently received a lot of attention as it arises in numerous situations in practice. Some applications being cloud computing systems where the arrival rates of new jobs fluctuate over time, or the time-varying capacity as encountered in power-aware systems or ...
Ina Maria Verloop
exaly   +4 more sources

A Whittle Index Approach to Minimizing Age of Multi-Packet Information in IoT Network

IEEE Access, 2021
Age of information (AoI) captures the freshness of information and has been used broadly as an important performance metric in big data analytics in the Internet of Things (IoT).
Mianlong Chen, Kui Wu, Linqi Song
doaj   +1 more source

Distributed learning algorithm with synchronized epochs for dynamic spectrum access in unknown environment using multi-user restless multi-armed bandit

Journal of King Saud University: Computer and Information Sciences, 2022
Dynamic spectrum access using cognitive radio has many application areas like smart-grid, Internet of Things, and various other device-to-device communication paradigms.
Himanshu Agrawal, Krishna Asawa
doaj   +1 more source

Deadline scheduling as restless bandits [PDF]

2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016
The problem of stochastic deadline scheduling is considered. A constrained Markov decision process model is introduced in which jobs arrive randomly at a service center with stochastic job sizes, rewards, and completion deadlines. The service provider faces random processing costs, convex non-completion penalties, and a capacity constraint that limits ...
Zhe Yu 0001, Yunjian Xu, Lang Tong 0001
openaire   +2 more sources

Approximations of the Restless Bandit Problem [PDF]

CoRR, 2017
The multi-armed restless bandit problem is studied in the case where the pay-off distributions are stationary $φ$-mixing. This version of the problem provides a more realistic model for most real-world applications, but cannot be optimally solved in practice, since it is known to be PSPACE-hard.
Grunewalder, Steffen, Khaleghi, Azadeh
openaire   +4 more sources