6 citations · 6 across the 2 of their papers we have counts for
4 papers
CT-DQN: Control-Tutored Deep Reinforcement Learning
Francesco De Lellis, Marco Coraggio, Giovanni Russo +2
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Contr…
External control of a genetic toggle switch via Reinforcement Learning
Sara Maria Brancato, Francesco De Lellis, Davide Salzano +2
We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach. To overcome the data efficiency problem that…
Individual- and pair-based models of epidemic spreading: master equations and analysis of their forecasting capabilities
Federico Malizia, Luca Gallo, Mattia Frasca +2
Mathematical modeling of disease spreading plays a crucial role in understanding, controlling and preventing epidemic outbreaks. In a microscopic description of the propagation of…
Intermittent yet coordinated regional strategies can alleviate the COVID-19 epidemic: a network model of the Italian case
Fabio Della Rossa, Davide Salzano, Anna Di Meglio +10
The COVID-19 epidemic that emerged in Wuhan China at the end of 2019 hit Italy particularly hard, yielding the implementation of strict national lockdown rules (Phase 1). There is…