1 citations · 1 across the 4 of their papers we have counts for
4 papers
Restless Bandits with Individual Penalty Constraints: Near-Optimal Indices and Deep Reinforcement Learning
Nida Zamir, I-Hong Hou
This paper investigates the Restless Multi-Armed Bandit (RMAB) framework under individual penalty constraints to address resource allocation challenges in dynamic wireless networke…
Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling
Nida Zamir, I-Hong Hou
Scheduling in multi-channel wireless communication system presents formidable challenges in effectively allocating resources. To address these challenges, we investigate a multi-re…
Distributed No-Regret Learning for Multi-Stage Systems with End-to-End Bandit Feedback
I-Hong Hou
This paper studies multi-stage systems with end-to-end bandit feedback. In such systems, each job needs to go through multiple stages, each managed by a different agent, before gen…
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making
Xin Chen, I-Hong Hou
This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core…