no code implementations • 13 Dec 2023 • Thomas Brilland, Guillaume Matheron, Laetitia Leduc, Yukihide Nakada
This article presents a new methodology for extracting intervals when a home is vacant from low-frequency electricity consumption data.
no code implementations • 22 Mar 2022 • Alexander Belikov, Guillaume Matheron, Johan Sassi
In this article we present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes.
no code implementations • 24 Apr 2020 • Guillaume Matheron, Nicolas Perrin, Olivier Sigaud
In this paper, we propose a new algorithm called "Plan, Backplay, Chain Skills" (PBCS) that combines motion planning and reinforcement learning to solve hard exploration environments.
no code implementations • 26 Nov 2019 • Guillaume Matheron, Nicolas Perrin, Olivier Sigaud
In environments with continuous state and action spaces, state-of-the-art actor-critic reinforcement learning algorithms can solve very complex problems, yet can also fail in environments that seem trivial, but the reason for such failures is still poorly understood.