Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Pasquali, Alex; Lomonaco, Vincenzo; Bacciu, Davide; Paganelli, Federica
Deep Reinforcement Learning for Network Slice Placement and the DeepNetSlice Toolkit Conference Forthcoming
Proceedings of the IEEE International Conference on Machine Learning for Communication and Networking 2024 (IEEE ICMLCN 2024), IEEE, Forthcoming.
@conference{nokey,
title = {Deep Reinforcement Learning for Network Slice Placement and the DeepNetSlice Toolkit},
author = {Alex Pasquali and Vincenzo Lomonaco and Davide Bacciu and Federica Paganelli},
year = {2024},
date = {2024-05-05},
urldate = {2024-05-05},
booktitle = {Proceedings of the IEEE International Conference on Machine Learning for Communication and Networking 2024 (IEEE ICMLCN 2024)},
publisher = {IEEE},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Lucchesi, Nicolò; Carta, Antonio; Lomonaco, Vincenzo; Bacciu, Davide
Avalanche RL: a Continual Reinforcement Learning Library Conference
Proceedings of the 21st International Conference on Image Analysis and Processing (ICIAP 2021), 2022.
@conference{Lucchesi2022,
title = {Avalanche RL: a Continual Reinforcement Learning Library},
author = {Nicolò Lucchesi and Antonio Carta and Vincenzo Lomonaco and Davide Bacciu},
url = {https://arxiv.org/abs/2202.13657, Arxiv},
year = {2022},
date = {2022-05-23},
urldate = {2022-05-23},
booktitle = {Proceedings of the 21st International Conference on Image Analysis and Processing (ICIAP 2021)},
abstract = {Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch and supports any OpenAI Gym environment. Its design is based on Avalanche, one of the more popular continual learning libraries, which allow us to reuse a large number of continual learning strategies and improve the interaction between reinforcement learning and continual learning researchers. Additionally, we propose Continual Habitat-Lab, a novel benchmark and a high-level library which enables the usage of the photorealistic simulator Habitat-Sim for CRL research. Overall, Avalanche RL attempts to unify under a common framework continual reinforcement learning applications, which we hope will foster the growth of the field. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch and supports any OpenAI Gym environment. Its design is based on Avalanche, one of the more popular continual learning libraries, which allow us to reuse a large number of continual learning strategies and improve the interaction between reinforcement learning and continual learning researchers. Additionally, we propose Continual Habitat-Lab, a novel benchmark and a high-level library which enables the usage of the photorealistic simulator Habitat-Sim for CRL research. Overall, Avalanche RL attempts to unify under a common framework continual reinforcement learning applications, which we hope will foster the growth of the field.
Semola, Rudy; Moro, Lorenzo; Bacciu, Davide; Prati, Enrico
Deep Reinforcement Learning Quantum Control on IBMQ Platforms and Qiskit Pulse Inproceedings
In: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 759-762, 2022.
@inproceedings{qskit2022,
title = {Deep Reinforcement Learning Quantum Control on IBMQ Platforms and Qiskit Pulse},
author = {Rudy Semola and Lorenzo Moro and Davide Bacciu and Enrico Prati},
doi = {10.1109/QCE53715.2022.00108},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 IEEE International Conference on Quantum Computing and Engineering (QCE)},
pages = {759-762},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
