12 citations · 64 across the 36 of their papers we have counts for
13 papers · 1 filter
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health
Aditya Mate, Lovish Madaan, Aparna Taneja +6
The widespread availability of cell phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. This paper describes our work t…
Facilitating human-wildlife cohabitation through conflict prediction
Susobhan Ghosh, Pradeep Varakantham, Aniket Bhatkhande +6
With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (an…
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning
Jackson A. Killian, Lily Xu, Arpita Biswas +1
We introduce robustness in \textit{restless multi-armed bandits} (RMABs), a popular model for constrained resource allocation among independent stochastic processes (arms). Nearly…
Q-Learning Lagrange Policies for Multi-Action Restless Bandits
Jackson A. Killian, Arpita Biswas, Sanket Shah +1
Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which independent processes are managed. However, previous wor…
Robust Reinforcement Learning Under Minimax Regret for Green Security
Lily Xu, Andrew Perrault, Fei Fang +2
Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers. Importantly,…
Contingency-Aware Influence Maximization: A Reinforcement Learning Approach
Haipeng Chen, Wei Qiu, Han-Ching Ou +2
The influence maximization (IM) problem aims at finding a subset of seed nodes in a social network that maximize the spread of influence. In this study, we focus on a sub-class of…