Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Control of chaotic systems by deep reinforcement learning

Abstract : Deep reinforcement learning (DRL) is applied to control a nonlinear, chaotic system governed by the one-dimensional Kuramoto-Sivashinsky (KS) equation. DRL uses reinforcement learning principles for the determination of optimal control solutions and deep neural networks for approximating the value function and the control policy. Recent applications have shown that DRL may achieve superhuman performance in complex cognitive tasks. In this work, we show that using restricted localized actuation, partial knowledge of the state based on limited sensor measurements and model-free DRL controllers, it is possible to stabilize the dynamics of the KS system around its unstable fixed solutions, here considered as target states. The robustness of the controllers is tested by considering several trajectories in the phase space emanating from different initial conditions; we show that DRL is always capable of driving and stabilizing the dynamics around target states. The possibility of controlling the KS system in the chaotic regime by using a DRL strategy solely relying on local measurements suggests the extension of the application of RL methods to the control of more complex systems such as drag reduction in bluff-body wakes or the enhancement/diminution of turbulent mixing.
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-02406677
Contributeur : Limsi Publications Connectez-vous pour contacter le contributeur
Soumis le : samedi 26 décembre 2020 - 18:43:07
Dernière modification le : mardi 4 janvier 2022 - 06:38:01
Archivage à long terme le : : lundi 29 mars 2021 - 16:46:53

Fichier

1906.07672.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Michele Alessandro Bucci, Onofrio Semeraro, Alexandre Allauzen, Guillaume Wisniewski, Laurent Cordier, et al.. Control of chaotic systems by deep reinforcement learning. Proceedings of the Royal Society of London. Series A, Mathematical and physical sciences, Royal Society, The, In press, 475 (2231), pp.1-20. ⟨10.1098/rspa.2019.0351⟩. ⟨hal-02406677⟩

Partager

Métriques

Les métriques sont temporairement indisponibles