Francesco De Lellis

RTDA - assistant professor

Contacts

Department of Electrical Engineering and Information Technology

University of Naples Federico II

Via Claudio 21, 80125, Naples, Italy

Telephone: +390817683607

Email: francesco.delellis@unina.it; francesco.delellis.93@gmail.com

Short Bio

Francesco De Lellis obtained his master degree in Automation Engineering at the University of Naples Federico II in October 2019. His master thesis focused on the development of model-based control-tutored reinforcement learning providing a solution of the herding problem.

Francesco also obtained his Ph.D. in Information Technology and Electrical Engineering at the University of Naples Federico II in May 2023. During his Ph.D. project Francesco has been advised by the prof.s Mario di Bernardo, Giovanni Russo and Mirco Musolesi. The core of Francesco's research deals with the application of non linear control theory and reinforcement learning for the development of new methodologies for the control of multi-agent complex systems.

Under the alias Redoken, Francesco is also an electronic music producer. He relased many tunes with well established record label such as Artist Intelligent Agency, DubstepGutter, Stereofox, Riddim Network and many more.

Publications

I. Napolitano, S. Covone, A. Lama, F. De Lellis, M. di Bernardo, "Hierarchical Learning-Based Control for Multi-Agent Shepherding of Stochastic Autonomous Agents", submitted to IEEE Transaction on Control Systems Technology, 2025.
A. Grotta, F. De Lellis, "Online Phase Estimation of Human Oscillatory Motion Using Deep Learning", accepted for presentation to IEEE Conference on Systems Man and Cybernetics (SMC), arXiv:2505.02668, 2025.
S. Covone, I. Napoitano, F. De Lellis, M. di Bernardo, "Hierarchical Policy-Gradient Reinforcement Learning for Multi-Agent Shepherding Control of Non-Cohesive Targets", submitted to IEEE Conference on Decision and Control (CDC), arXiv:2504.02479, 2025.
I. Napolitano, A. Lama, F. De Lellis, M. di Bernardo, "Emergent Cooperative Strategies for Multi-Agent Shepherding via Reinforcement Learning", European Control Conference (ECC), pp. 1809-1814, arxiv:2411.05454, 2025.
A. Grotta, M. Coraggio, A. Spallone, F. De Lellis, M. di Bernardo, "Learning-based cognitive architecture for enhancing coordination in human groups", Cyber-Physical Human Systems (CPHS), IFAC-PapersOnLine, 58(30), pp. 37-42, 2024.
F. De Lellis, M. Coraggio, N. C. Foster, R. Villa, C. Becchio, M. di Bernardo, “Data-driven architecture to encode information in the kinematics of robots and artificial avatars”, IEEE Control Systems Letters and Conference on Decision and Control (CDC), vol. 8, pp. 1919-1924, 2024.
S. M. Brancato, D. Salzano, F. De Lellis, D. Fiore, G. Russo, M. di Bernardo, "In vivo learning-based control of microbial populations density in bioreactors", Learning for Dynamics and Control (L4DC), Proceedings of Machine Learning Research (PMLR), 242:941-953, 2024.
F. De Lellis, M. Coraggio, G. Russo, M. Musolesi, M. di Bernardo, "Guaranteeing Control Performance via Reward Shaping in Reinforcement Learning", IEEE Transactions on Control Systems Technology, vol. 32, no. 6, pp. 2102-2113, 2024.
F. De Lellis, M. Coraggio, G. Russo, M. Musolesi, M. di Bernardo, "CT-DQN: Control-Tutored Deep Reinforcement Learning", Learning for Dynamics and Control Conference (L4DC), Proceedings of Machine Learning Research (PMLR), 211, pp. 941-953, 2023.
S. M. Brancato, F. De Lellis, D. Salzano, G. Russo, M. di Bernardo, "External control of genetic toggle switch via Reinforcement Learning", European Control Conference (ECC), pp. 1-6, 2023.
F. De Lellis, M. Coraggio, G. Russo, M. Musolesi, M. di Bernardo "Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model Based Control", Learning for Dynamics and Control Conference (L4DC), Proceedings of Machine Learning Research (PMLR), 168, pp. 1048-1059, 2022.
M. Coraggio, S. Xie, F. De Lellis, G. Russo, M. di Bernardo, "Intermittent non-pharmaceutical strategies to mitigate the COVID-19 epidemic in a network model of Italy via constrained optimization", IEEE Conference on Decision and Control (CDC), pp. 3538-3543, 2021.
F. De Lellis, F. Auletta, G. Russo, P. De Lellis, M. di Bernardo, "An Application of Control-Tutored Reinforcement Learning to the Herding Problem", IEEE International Workshop on Cellular Nanoscale Networks and their Applications (CNNA), pp. 1-4, 2021.
F. De Lellis, G. Russo, M. di Bernardo, "Tutoring Reinforcement Learning via Feedback Control", European Control Conference (ECC), pp. 580-585, 2021.
F. Della Rossa, D. Salzano, A. Di Meglio, F. De Lellis, M. Coraggio, C. Calabrese, A. Guarino, R. Cardona-Rivera, P. De Lellis, D. Liuzza, F. Lo Iudice, G. Russo, M. di Bernardo, "A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic", Nature Communications, 11, 5106, 2020.

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