6 citations · 14 across the 8 of their papers we have counts for
8 papers
A bioreactor-based architecture for in vivo model-based and sim-to-real learning control of microbial consortium composition
Sara Maria Brancato, Davide Salzano, Davide Fiore +3
Microbial consortia offer significant biotechnological advantages over monocultures for bioproduction. However, industrial deployment is hampered by the lack of scalable architectu…
In vivo learning-based control of microbial populations density in bioreactors
Sara Maria Brancato, Davide Salzano, Francesco De Lellis +3
A key problem toward the use of microorganisms as bio-factories is reaching and maintaining cellular communities at a desired density and composition so that they can efficiently c…
Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning
Francesco De Lellis, Marco Coraggio, Giovanni Russo +2
In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performanc…
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 non-pharmaceutical strategies to mitigate the COVID-19 epidemic in a network model of Italy via constrained optimization
Marco Coraggio, Shihao Xie, Francesco De Lellis +2
This paper is concerned with the design of intermittent non-pharmaceutical strategies to mitigate the spread of the COVID-19 epidemic exploiting network epidemiological models. Spe…
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…