Restless multi-arm bandits (RMABs) are a model for sequentially allocating a limited number
of resources to agents modeled as Markov Decision Processes. RMABs have applications in …

Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a
promising avenue for addressing allocation problems with resource constraints and …

Beyond "To Act or Not to Act": Fast Lagrangian Approaches to General Multi-Action Restless
Bandits Page 1 Beyond "To Act or Not to Act": Fast Lagrangian Approaches to General Multi-Action …

We study the problem of planning restless multi-armed bandits (RMABs) with multiple
actions. This is a popular model for multi-agent systems with applications like multi-channel …

The growing demand for radio frequency (RF) spectrum access poses new challenges for
next-generation radar systems. To operate in a crowded electromagnetic environment …

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 …

For public health programs with limited resources, the ability to predict how behaviors
change over time and in response to interventions is crucial for deciding when and to whom …

Restless multi-armed bandits (RMABs) are a popular framework for algorithmic decision
making in sequential settings with limited resources. RMABs are increasingly being used for …

Restless multi-armed bandits (RMABs) generalize the multi-armed bandits where each arm
exhibits Markovian behavior and transitions according to their transition dynamics. Solutions …

Prior works have explored multi-armed bandit (MAB) algorithms for the selection of optimal
beams for millimeter-wave (mmW) communications between base station and mobile users …