PORTO @ 
Archivio Istituzionale della Ricerca
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishWe propose an efficient technique for performing data-driven optimal control of discrete-time systems. In particular, we show that log-sum-exp ($lse$) neural networks, which are smooth and convex universal approximators of convex functions, can be efficiently used to approximate Q-factors arising from finite-horizon optimal control problems with continuous state space. The key advantage of these networks over classical approximation techniques is that they are convex and hence readily amenable to efficient optimization.
Efficient model-free Q-factor approximation in value space via log-sum-exp neural networks / Calafiore, Giuseppe Carlo; Possieri, Corrado. - ELETTRONICO. - (2020). ((Intervento presentato al convegno European Control Conference (ECC2020) tenutosi a Saint Petersburg, Russia nel 12-15 May, 2020 [10.23919/ECC51009.2020.9143765].
| Titolo: | Efficient model-free Q-factor approximation in value space via log-sum-exp neural networks | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | We propose an efficient technique for performing data-driven optimal control of discrete-time sys...tems. In particular, we show that log-sum-exp ($lse$) neural networks, which are smooth and convex universal approximators of convex functions, can be efficiently used to approximate Q-factors arising from finite-horizon optimal control problems with continuous state space. The key advantage of these networks over classical approximation techniques is that they are convex and hence readily amenable to efficient optimization. | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| reinLSE_final.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri | |
| Calafiore-Efficient.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2837797
Copyright © 2022