Search Results for author: Jackson A. Killian

Found 5 papers, 2 papers with code

Robust Restless Bandits: Tackling Interval Uncertainty with Deep Reinforcement Learning

1 code implementation4 Jul 2021 Jackson A. Killian, Lily Xu, Arpita Biswas, Milind Tambe

To make RMABs more useful in settings with uncertain dynamics: (i) We introduce the Robust RMAB problem and develop solutions for a minimax regret objective when transitions are given by interval uncertainties; (ii) We develop a double oracle algorithm for solving Robust RMABs and demonstrate its effectiveness on three experimental domains; (iii) To enable our double oracle approach, we introduce RMABPPO, a novel deep reinforcement learning algorithm for solving RMABs.

Multi-agent Reinforcement Learning reinforcement-learning

Q-Learning Lagrange Policies for Multi-Action Restless Bandits

1 code implementation22 Jun 2021 Jackson A. Killian, Arpita Biswas, Sanket Shah, Milind Tambe

Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which $N$ independent processes are managed.

Multi-Armed Bandits Q-Learning

Envisioning Communities: A Participatory Approach Towards AI for Social Good

no code implementations4 May 2021 Elizabeth Bondi, Lily Xu, Diana Acosta-Navas, Jackson A. Killian

We argue that AI for social good ought to be assessed by the communities that the AI system will impact, using as a guide the capabilities approach, a framework to measure the ability of different policies to improve human welfare equity.

Collapsing Bandits and Their Application to Public Health Interventions

no code implementations5 Jul 2020 Aditya Mate, Jackson A. Killian, Haifeng Xu, Andrew Perrault, Milind Tambe

(ii) We exploit the optimality of threshold policies to build fast algorithms for computing the Whittle index, including a closed-form.

Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

no code implementations5 Feb 2019 Jackson A. Killian, Bryan Wilder, Amit Sharma, Daksha Shah, Vinod Choudhary, Bistra Dilkina, Milind Tambe

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.

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