3 papers
cs.LG2025
Networked Restless Multi-Arm Bandits with Reinforcement Learning
Hanmo Zhang, Zenghui Sun, Kai Wang
Restless Multi-Armed Bandits (RMABs) are a powerful framework for sequential decision-making, widely applied in resource allocation and intervention optimization challenges in publ…
cs.LG2025
Neural Index Policies for Restless Multi-Action Bandits with Heterogeneous Budgets
Himadri S. Pandey, Kai Wang, Gian-Gabriel P. Garcia
Restless multi-armed bandits (RMABs) provide a scalable framework for sequential decision-making under uncertainty, but classical formulations assume binary actions and a single gl…
cs.LG2025
Non-Stationary Restless Multi-Armed Bandits with Provable Guarantee
Yu-Heng Hung, Ping-Chun Hsieh, Kai Wang
Online restless multi-armed bandits (RMABs) typically assume that each arm follows a stationary Markov Decision Process (MDP) with fixed state transitions and rewards. However, in…