On learning Whittle index policy for restless bandits with scalable regret
N Akbarzadeh, A Mahajan - IEEE Transactions on Control of …, 2023 - ieeexplore.ieee.org
Reinforcement learning is an attractive approach to learn good resource allocation and
scheduling policies based on data when the system model is unknown. However, the …
scheduling policies based on data when the system model is unknown. However, the …
Limited resource allocation in a non-Markovian world: the case of maternal and child healthcare
The success of many healthcare programs depends on participants' adherence. We
consider the problem of scheduling interventions in low resource settings (eg, placing timely …
consider the problem of scheduling interventions in low resource settings (eg, placing timely …
[PDF][PDF] Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Restless multi-arm bandits (RMABs) is a class of resource allocation problems with broad
application in areas such as healthcare, online advertising, and anti-poaching. We explore …
application in areas such as healthcare, online advertising, and anti-poaching. We explore …
Towards Zero Shot Learning in Restless Multi-armed Bandits
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 …
application in areas such as healthcare, online advertising, and anti-poaching, have recently …
[图书][B] Restless Bandits: Indexability, Computation of Whittle Index and Learning
N Akbarzadeh - 2022 - search.proquest.com
Restless bandits are a class of sequential resource allocation problems concerned with
allocating one or more resources among several alternative processes where the evolution …
allocating one or more resources among several alternative processes where the evolution …