Results 11 to 20 of about 257 (142)

Global Rewards in Restless Multi-Armed Bandits

Advances in Neural Information Processing Systems 37
Restless multi-armed bandits (RMAB) extend multi-armed bandits so pulling an arm impacts future states. Despite the success of RMABs, a key limiting assumption is the separability of rewards into a sum across arms. We address this deficiency by proposing restless-multi-armed bandit with global rewards (RMAB-G), a generalization of RMABs to global non ...
Naveen Raman 0001, Zheyuan Ryan Shi, Fei Fang 0001   +2 more
openaire   +4 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   +2 more sources

Uncertainty-of-Information Scheduling: A Restless Multiarmed Bandit Framework

IEEE Transactions on Information Theory, 2022
This paper proposes using the uncertainty of information (UoI), measured by Shannon's entropy, as a metric for information freshness. We consider a system in which a central monitor observes multiple binary Markov processes through a communication channel. The UoI of a Markov process corresponds to the monitor's uncertainty about its state.
Gongpu Chen, Soung Chang Liew, Yulin Shao   +2 more
openaire   +3 more sources

Fresh Caching of Dynamic Contents using Restless Multi-armed Bandits

2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS)
We consider a dynamic content caching problem wherein the contents get updated at a central server, and local copies of a subset of contents are cached at a local cache associated with a Base station (BS). When a content request arrives, based on whether the content is in the local cache, the BS can decide whether to fetch the content from the central ...
Ankita Koley, Chandramani Singh
openaire   +3 more sources

Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization

, 2023
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently been studied from a multi-agent reinforcement learning perspective. Prior RMAB research suffers from several limitations, e.g., it fails to adequately address continuous ...
Yunfan Zhao, Nikhil Behari, Edward Hughes 0001, Edwin Zhang, Dheeraj Nagaraj, Karl Tuyls, Aparna Taneja, Milind Tambe   +7 more
openaire   +4 more sources

Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback

CoRR
Restless multi-armed bandits (RMAB) play a central role in modeling sequential decision making problems under an instantaneous activation constraint that at most B arms can be activated at any decision epoch. Each restless arm is endowed with a state that evolves independently according to a Markov decision process regardless of being activated or not.
Guojun Xiong, Jian Li 0008
openaire   +4 more sources

Best Arm Identification in Restless Markov Multi-Armed Bandits

IEEE Transactions on Information Theory, 2023
We study the problem of identifying the best arm in a multi-armed bandit environment when each arm is a time-homogeneous and ergodic discrete-time Markov process on a common, finite state space. The state evolution on each arm is governed by the arm's transition probability matrix (TPM).
P. N. Karthik, Kota Srinivas Reddy, Vincent Y. F. Tan   +2 more
openaire   +2 more sources

Expanding impact of mobile health programs: SAHELI for maternal and child care

AI Magazine, Volume 44, Issue 4, Page 363-376, Winter 2023., 2023
Abstract Underserved communities face critical health challenges due to lack of access to timely and reliable information. Nongovernmental organizations are leveraging the widespread use of cellphones to combat these healthcare challenges and spread preventative awareness.
Shresth Verma, Gargi Singh, Aditya Mate, Paritosh Verma, Sruthi Gorantla, Neha Madhiwalla, Aparna Hegde, Divy Thakkar, Manish Jain, Milind Tambe, Aparna Taneja   +10 more
wiley   +1 more source

Climate‐related armed conflict and communities' resistance to Rural Grazing Area settlement policy in Nigeria's Middlebelt

Conflict Resolution Quarterly, Volume 41, Issue 2, Page 121-141, Winter 2023., 2023
Abstract In Nigeria, resource contests have sparked unending ecological conflict. As a result, conflict resolution measures have been proposed to mitigate climate‐related conflict. However, the acceptance of such policies is hampered by ethnic suspicions, communities' exclusion, religious sensitivities, and a lack of political will.
John Sunday Ojo
wiley   +1 more source

Distributed reinforcement learning for dynamic spectrum allocation in cognitive radio‐based internet of things

IET Networks, Volume 11, Issue 6, Page 207-220, November 2022., 2022
Abstract Cognitive Radio (CR) with other advancements such as the Internet of things and machine learning has recently emerged as the main involved technique to use spectrum in an efficient manner. It can access the spectrum in a fully dynamic way and exploit the unused spectrum resources without creating any harm to cognitive users. In this paper, the
Jamal Elhachmi
wiley   +1 more source