Recherche - Mines Paris Accéder directement au contenu

Filtrer vos résultats

50 résultats
Image document

Deep Reinforcement Learning for Optimal Energy Management of Multi-energy Smart Grids

Dhekra Bousnina , Gilles Guerassimoff
Lecture Notes in Computer Science, 2022, pp.15 - 30. ⟨10.1007/978-3-030-95470-3_2⟩
Article dans une revue hal-03587262v1
Image document

Path planning for a maritime surface ship based on Deep Reinforcement Learning and weather forecast

Eva Artusi , Fabien Chaillan , Aldo Napoli
IEEE/MTS OCEANS 2021, Sep 2021, San Diego, CA, United States
Communication dans un congrès hal-03726769v1
Image document

Apprentissage par renforcement du contrôle d’un véhicule autonome à partir de la vision

Marin Toromanoff
Robotique [cs.RO]. Université Paris sciences et lettres, 2021. Français. ⟨NNT : 2021UPSLM020⟩
Thèse tel-03347567v1
Image document

Is Deep Reinforcement Learning Really Superhuman on Atari?

Marin Toromanoff , Emilie Wirbel , Fabien Moutarde
Deep Reinforcement Learning Workshop of 39th Conference on Neural Information Processing Systems (Neurips'2019), Dec 2019, Vancouver, Canada
Communication dans un congrès hal-02368263v1

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

Zhaojun Hao , Francesco Di Maio , Enrico Zio
Reliability Engineering and System Safety, 2023, 235, pp.109231. ⟨10.1016/j.ress.2023.109231⟩
Article dans une revue hal-04099955v1

Multi-energy optimization in Smart Grids: a Deep Reinforcement Learning approach

Dhekra Bousnina , Gilles Guerassimoff
SophI.A Summit, Nov 2020, Sophia-Antipolis, France
Communication dans un congrès hal-03021016v1

Deep Reinforcement Learning for optimal energy management of multi-energy Smart Grids

Dhekra Bousnina , Gilles Guerassimoff
LOD 2021 : the 7th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science, Oct 2021, Grasmere, United Kingdom
Communication dans un congrès hal-03587180v1

Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews

Luca Pinciroli , Piero Baraldi , Guido Ballabio , Michele Compare , Enrico Zio
Energies, 2021, 14 (20), pp.6743. ⟨10.3390/en14206743⟩
Article dans une revue hal-03481360v1

Photovoltaic self-consumption optimization for Home Microgrid: A Deep Reinforcement Learning approach

Mohamed Saâd EL HARRAB , Michel Nakhla
EURO 2022, 32nd EURO Conference, Association of European Operational Research Societies, Jul 2022, Espoo, Finland
Communication dans un congrès hal-03746179v1

Parallel Bootstrap-Based On-Policy Deep Reinforcement Learning for Continuous Fluid Flow Control Applications

Jonathan Viquerat , Elie Hachem
Fluids, 2023, 8 (7), pp.208. ⟨10.3390/fluids8070208⟩
Article dans une revue hal-04328631v1

Decision support system based on Deep Reinforcement Learning for war ships facing asymmetric threats

Eva Artusi , Fabien Chaillan , Aldo Napoli
Undersea Defence Technology (UDT) 2022, Jun 2022, Rotterdam, Netherlands
Communication dans un congrès hal-03696075v1

Deep Reinforcement Learning Based Home Energy Management System (HEMS)

Mohamed Saâd EL HARRAB , Michel Nakhla
OLA 2022, International Conference on Optimization & Learning, Jul 2022, Syracuse, Italy
Communication dans un congrès hal-03746181v1

A deep reinforcement learning-based method for predictive management of demand response in natural gas pipeline networks

Lin Fan , Huai Su , Enrico Zio , Lixun Chi , Li Zhang , et al.
Journal of Cleaner Production, 2022, 335, pp.130274. ⟨10.1016/j.jclepro.2021.130274⟩
Article dans une revue hal-03907581v1
Image document

On the coupling of deep reinforcement learning and computational fluid dynamics

Hassan Ghraieb
Fluid mechanics [physics.class-ph]. Université Paris sciences et lettres, 2022. English. ⟨NNT : 2022UPSLM037⟩
Thèse tel-04043187v1
Image document

Direct shape optimization through deep reinforcement learning

Jonathan Viquerat , Mines Paristech , Jean Rabault , Alexander Kuhnle , Hassan Ghraieb , et al.
2021
Pré-publication, Document de travail hal-02401468v2

OPTIMAL MULTI-ENERGY MANAGEMENT IN SMART ENERGY SYSTEMS: A DEEP REINFORCEMENT LEARNING APPROACH AND A CASE-STUDY ON A FRENCH ECO-DISTRICT

Dhekra Bousnina , Gilles Guerassimoff
Smart Energy Systems, International Conference, Energy Cluster Denmark; AAlborg University Denmark, Sep 2023, Copenhaguen, Denmark
Communication dans un congrès hal-04323498v1

A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning

Lin Fan , Huai Su , Wei Wang , Enrico Zio , Li Zhang , et al.
Reliability Engineering and System Safety, 2022, 225, pp.108613. ⟨10.1016/j.ress.2022.108613⟩
Article dans une revue hal-03906637v1

Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning

Luca Pinciroli , Piero Baraldi , Guido Ballabio , Michele Compare , Enrico Zio
Renewable Energy, 2022, 183, pp.752-763. ⟨10.1016/j.renene.2021.11.052⟩
Article dans une revue hal-03481413v1
Image document

Apprentissage par renforcement profond dans une architecture cognitive pour l'aide à la conduite de missions navales

Eva Artusi
Risques. Université Paris sciences et lettres, 2021. Français. ⟨NNT : 2021UPSLM074⟩
Thèse tel-03882771v1
Image document

Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows

Hassan Ghraieb , Jonathan Viquerat , Aurélien Larcher , P. Meliga , Elie Hachem
Physical Review Fluids, 2021
Article dans une revue hal-03027908v2
Image document

Investigating gas furnace control practices with reinforcement learning

M Renault , J Viquerat , P Meliga , G.-A Grandin , N Meynet , et al.
International Journal of Heat and Mass Transfer, 2023, 209, pp.124147. ⟨10.1016/j.ijheatmasstransfer.2023.124147⟩
Article dans une revue hal-04245154v1
Image document

Policy-based optimization: single-step policy gradient method seen as an evolution strategy

Jonathan Viquerat , Régis Duvigneau , Philippe Meliga , A Kuhnle , Elie Hachem
Neural Computing and Applications, 2022, ⟨10.1007/s00521-022-07779-0⟩
Article dans une revue hal-03432655v1
Image document

Single-step deep reinforcement learning for two- and three-dimensional optimal shape design

H. Ghraieb , J. Viquerat , A. Larcher , P. Meliga , E. Hachem
AIP Advances, 2022, 12 (8), pp.085108. ⟨10.1063/5.0097241⟩
Article dans une revue hal-03825017v1
Image document

Deep reinforcement learning for the control of conjugate heat transfer

Elie Hachem , Hassan Ghraieb , Jonathan Viquerat , Aurélien Larcher , P. Meliga
Journal of Computational Physics, 2021, 436, pp.110317. ⟨10.1016/j.jcp.2021.110317⟩
Article dans une revue hal-03027923v2
Image document

LEARNING FROM DEMONSTRATIONS WITH SACR2: SOFT ACTOR-CRITIC WITH REWARD RELABELING

Jesus Bujalance , Raphael Chekroun , Fabien Moutarde
'Deep Reinforcement Learning' workshop of the 35th Conference on Neural Information Processing Systems (NeurIPS'2021), Dec 2021, Virtual, United States
Communication dans un congrès hal-03519790v1
Image document

Deep Reinforcement Learning for Optimal Energy Management in Smart Multi-Energy Systems

Dhekra Bousnina
Electric power. Université Paris sciences et lettres, 2023. English. ⟨NNT : 2023UPSLM064⟩
Thèse tel-04558019v1

Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems

Fatima Ezzahra Achamrah , Ali Attajer
International Journal of Production Research, 2023, pp.1-23. ⟨10.1080/00207543.2023.2240433⟩
Article dans une revue hal-04171570v1
Image document

Dynamic metasurface control using Deep Reinforcement Learning

Ying Zhao , Liang Li , Stéphane Lanteri , Jonathan Viquerat
Mathematics and Computers in Simulation, 2023, 197, pp.377-395. ⟨10.1016/j.matcom.2022.02.016⟩
Article dans une revue hal-03924777v1
Image document

A review on deep reinforcement learning for fluid mechanics: an update

J Viquerat , P Meliga , A Larcher , E Hachem
Physics of Fluids, 2022, 34 (11), pp.111301. ⟨10.1063/5.0128446⟩
Article dans une revue hal-03432654v2

Direct shape optimization through deep reinforcement learning

Jonathan Viquerat , Jean Rabault , Alexander Kuhnle , Hassan Ghraieb , Aurélien Larcher , et al.
2020
Pré-publication, Document de travail hal-02428728v1