Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning-Reference-Cited by-同舟云学术

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Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

Published:2024-09 Issue: Volume:249 Page:123686
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Reymond Mathieu^ORCID,Hayes Conor F.^ORCID,Willem Lander^ORCID,Rădulescu Roxana^ORCID,Abrams Steven^ORCID,Roijers Diederik M.^ORCID,Howley Enda^ORCID,Mannion Patrick^ORCID,Hens Niel^ORCID,Nowé Ann^ORCID,Libin Pieter^ORCID

Funder

Vlaamse Overheid

Vrije Universiteit Brussel

European Research Council

Fonds Wetenschappelijk Onderzoek Vlaanderen

University of Galway

Universiteit Antwerpen

Publisher

Elsevier BV

Reference36 articles.

1. Dynamic weights in multi-objective deep reinforcement learning;Abels,2019

2. Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories;Abrams;Epidemics,2021

3. Optimistic linear support and successor features as a basis for optimal policy transfer;Alegre,2022

4. Sample-efficient multi-objective learning via generalized policy improvement prioritization;Alegre,2023

5. The mathematical theory of infectious diseases and its applications;Bailey,1975

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