… most data-driven intervention policies rely on heuristics. In this paper, we study how
reinforcement learning (RL) can be used to optimize mitigation policies that minimize the economic …

… study of optimal policy making strategies that apply different … -model as the reinforcement
learning framework with particular … of reinforcement learning algorithms based on the COVID-19 …

… strategies. The first agent placed long lockdowns to reduce the initial spread of the disease,
followed by cyclical and shorter lockdowns to mitigate … as the COVID-19 pandemic caused by …

… approach to optimize the distribution of the COVID-19 vaccine… evaluating their policy against
a naive distribution policy. Ohi … to optimize mitigation policies within the pandemic simulator. …

… Current research focuses on optimizing policies with respect to a single objective… policies.
To enhance future decision-making, we propose a deep multi-objective reinforcement learning …

… to optimize mitigation policies aimed at minimizing the economic impact and the risk of COVID-19
… in a controlled pandemic simulator and learning to decide mitigation policies. Then we …

… In this paper, a GRU is introduced in an agent’s policy learning according to the … reinforcement
learning to optimize the redistribution of critical medical supplies throughout the covid-19 …

… reinforcement learning-based closed-loop control strategy as a decision support tool for
mitigating COVID-19… optimizing intervention policies. The focus of this paper is to present such a …

… The use of a proximity policy optimization algorithm is a key … In panel (a), we present a
weekly breakdown of COVID-19 … reinforcing social distancing policies to mitigate inter-regional …