Results 81 to 90 of about 110 (105)
Some of the next articles are maybe not open access.
Towards Zero Shot Learning in Restless Multi-armed Bandits
International Joint Conference on Autonomous Agents and Multiagent SystemsRestless 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 +7 more
openaire +2 more sources
Optimality of myopic policy for a class of monotone affine restless multi-armed bandits
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012We formulate a general class of restless multi-armed bandits with n independent and stochastically identical arms. Each arm is in a real-valued state s ∈ [s 0 , s max ]. Selecting an arm with state s yields an immediate reward with expectation R(s). The state of the arm that is selected stochastically jumps from its current value s to either s max or ...
Tara Javidi, Bhaskar Krishnamachari
exaly +2 more sources
Learning in Restless Multi-Armed Bandits using Adaptive Arm Sequencing Rules
2018 IEEE International Symposium on Information Theory (ISIT), 2018We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of $N$ arms to play, referred to as an active arm, and receives a random reward from a finite set of reward states. The reward state of the active arm transits according to an unknown Markovian dynamic.
Tomer Gafni, Kobi Cohen
openaire +1 more source
Time-Constrained Restless Multi-Armed Bandits with Applications to City Service Scheduling
International Joint Conference on Autonomous Agents and Multiagent SystemsMunicipalities maintain critical infrastructure through inspections, both proactive and in response to complaints. For example, the Chicago Department of Public Health (CDPH) periodically inspects 7000 food establishments to maintain the safety of food bought, sold, or prepared for public consumption. Restless multi-armed bandits (RMABs) appear to be a
Yi Mao, Andrew Perrault
openaire +2 more sources
Restless Multi-Armed Bandit in Opportunistic Scheduling
2021Kehao Wang, Lin Chen
openaire +1 more source
Multi-armed bandits with dependent arms
Machine Learning, 2023Yin Sun, Ness Shroff, Rahul Singh
exaly
Application of multi-armed bandits to dose-finding clinical designs
Artificial Intelligence in Medicine, 2023Masahiro Kojima
exaly
The Perils of Misspecified Priors and Optional Stopping in Multi-Armed Bandits
Frontiers in Artificial Intelligence, 2021Markus Loecher
exaly
MAB-OS: Multi-Armed Bandits Metaheuristic Optimizer Selection
Applied Soft Computing Journal, 2022Seyedali Mirjalili +2 more
exaly
On the Bias, Risk, and Consistency of Sample Means in Multi-armed Bandits
SIAM Journal on Mathematics of Data Science, 2021Aaditya Ramdas
exaly