no code implementations • 25 Jan 2023 • Mohammadreza Chamanbaz, Roland Bouffanais
At each iteration step of both algorithms, we first test the feasibility of a given test solution for each and every constraint associated with the sampled optimisation at hand, while also identifying those constraints that are violated.
no code implementations • 26 Sep 2022 • Peng Sun, Robert E. Kooij, Roland Bouffanais
In this paper, we propose closed-form analytical expressions to determine the minimum number of driver nodes that is needed to control a specific class of networks.
no code implementations • 25 Jan 2021 • Eduard G. Fedorov, Alexander V. Zhukov, Roland Bouffanais, Natalia N. Konobeeva, Evgeniya V. Boroznina, Boris A. Malomed, Hervé Leblond, Dumitru Mihalache, Mikhail B. Belonenko, Nikolay N. Rosanov, Thomas F. George
We present a study of the propagation of three-dimensional (3D) bipolar electromagnetic ultrashort pulses in an inhomogeneous array of semiconductor carbon nanotubes (CNTs) in the presence of a control high-frequency (HF) electric field.
Optics Pattern Formation and Solitons
no code implementations • 18 Nov 2018 • Thommen George Karimpanal, Roland Bouffanais
In this work, we describe a novel approach for reusing previously acquired knowledge by using it to guide the exploration of an agent while it learns new tasks.
no code implementations • 19 Jul 2018 • Thommen George Karimpanal, Roland Bouffanais
The idea of reusing information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample efficiency reinforcement learning agents.
no code implementations • 30 May 2017 • Thommen George Karimpanal, Roland Bouffanais
Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms.