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

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

Abstract: Highlights•Epidemic mitigation involves multiple criteria (mortality, economic cost, well-being).•Multi-objective RL (MORL) to explore the Pareto front of deconfinement strategies.•We investigate deconfinement strategies after the first Belgian lockdown of COVID-19.•Minimize both COVID-19 cases and societal burden; leads to many distinct trade-offs.•MORL brings essential insights to balance mitigation policies and help policy makers.