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Chance-Constrained Control with Lexicographic Deep Reinforcement Learning

Autor: Giuseppi, Alessandro, Pietrabissa, Antonio

Publikováno v: IEEE Control Systems Letters, vol. 4, no. 3, pp. 755-760, July 2020

This paper proposes a lexicographic Deep Reinforcement Learning (DeepRL)-based approach to chance-constrained Markov Decision Processes, in which the controller seeks to ensure that the probability of satisfying the constraint is above a given thresh

Externí odkaz: http://arxiv.org/abs/2010.09468

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6G in the Sky: On-Demand Intelligence at the Edge of 3D Networks

Autor: Strinati, Emilio Calvanese, Barbarossa, Sergio, Choi, Taesang, Pietrabissa, Antonio, Giuseppi, Alessandro, De Santis, Emanuele, Vidal, Josep, Becvar, Zdenek, Haustein, Thomas, Cassiau, Nicolas, Costanzo, Francesca, Kim, Junhyeong, Kim, Ilgyu

Publikováno v: ETRI Journal 2020

6G will exploit satellite, aerial and terrestrial platforms jointly to improve radio access capability and to unlock the support of on-demand edge cloud services in the three dimensional space (3D) by incorporating Mobile Edge Computing (MEC) functio

Externí odkaz: http://arxiv.org/abs/2010.09463

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Operations Management of Satellite Launch Centers

Autor: Tortorelli, Andrea, Giuseppi, Alessandro, Lisi, Federico, De Santis, Emanuele, Liberati, Francesco

Driven by the business potentialities of the satellite industry, the last years witnessed a massive increase of attention in the space industry. This sector has been always considered critical by national entities and international organizations worl

Externí odkaz: http://arxiv.org/abs/2001.09472

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