no code implementations • 31 Jan 2024 • Adrien Bolland, Gaspard Lambrechts, Damien Ernst
To compute near-optimal policies, it is essential in practice to include exploration terms in the learning objective.
no code implementations • 29 Jan 2024 • Samy Aittahar, Adrien Bolland, Guillaume Derval, Damien Ernst
We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets.
1 code implementation • NeurIPS 2023 • Pascal Leroy, Pablo G. Morato, Jonathan Pisane, Athanasios Kolios, Damien Ernst
We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning (MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a platform for benchmarking the scalability of cooperative MARL methods in real-world engineering applications.
1 code implementation • 20 Jun 2023 • Gaspard Lambrechts, Adrien Bolland, Damien Ernst
We then show that this informed objective consists of learning an environment model from which we can sample latent trajectories.
no code implementations • 6 Jun 2023 • Florent De Geeter, Damien Ernst, Guillaume Drion
We show that this new network can achieve performance comparable to other types of spiking networks in the MNIST benchmark and its variants, the Fashion-MNIST and the Neuromorphic-MNIST.
no code implementations • 11 May 2023 • Adrien Bolland, Gilles Louppe, Damien Ernst
First, we formulate direct policy optimization in the optimization by continuation framework.
no code implementations • 27 Jan 2023 • Thibaut Théate, Antonio Sutera, Damien Ernst
At its core, this idea consists in providing the consumer with a price signal which is evolving over time, in order to influence its consumption.
1 code implementation • 30 Dec 2022 • Thibaut Théate, Damien Ernst
Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome.
2 code implementations • 21 Nov 2022 • Pascal Leroy, Jonathan Pisane, Damien Ernst
In this paper, we identify the best learning scenario to train a team of agents to compete against multiple possible strategies of opposing teams.
no code implementations • 6 Aug 2022 • Gaspard Lambrechts, Adrien Bolland, Damien Ernst
In summary, this work shows that in its hidden states, a recurrent neural network approximating the Q-function of a partially observable environment reproduces a sufficient statistic from the history that is correlated to the relevant part of the belief for taking optimal actions.
no code implementations • 7 Jan 2022 • Florian Merchie, Damien Ernst
To evaluate the performances of the trained models, standard machine learning metrics were used, such as accuracy, precision and recall.
1 code implementation • 6 Jun 2021 • Thibaut Théate, Antoine Wehenkel, Adrien Bolland, Gilles Louppe, Damien Ernst
The results highlight the main strengths and weaknesses associated with each probability metric together with an important limitation of the Wasserstein distance.
Distributional Reinforcement Learning reinforcement-learning +2
no code implementations • 2 Jun 2021 • Gaspard Lambrechts, Florent De Geeter, Nicolas Vecoven, Damien Ernst, Guillaume Drion
This insight leads to the design of a novel way to initialise any recurrent cell connectivity through a procedure called "warmup" to improve its capability to learn arbitrarily long time dependencies.
1 code implementation • 20 May 2021 • Michaël Fonder, Damien Ernst, Marc Van Droogenbroeck
We use these cost volumes to leverage the visual spatio-temporal constraints imposed by motion and to make the network robust for varied scenes.
Ranked #2 on Monocular Depth Estimation on Mid-Air Dataset
no code implementations • 18 May 2021 • Robin Henry, Damien Ernst
Gym-ANM is a Python package that facilitates the design of reinforcement learning (RL) environments that model active network management (ANM) tasks in electricity networks.
no code implementations • 12 Apr 2021 • David Radu, Antoine Dubois, Mathias Berger, Damien Ernst
The accurate representation of variable renewable generation (RES, e. g., wind, solar PV) assets in capacity expansion planning (CEP) studies is paramount to capture spatial and temporal correlations that may exist between sites and impact both power system design and operation.
2 code implementations • 14 Mar 2021 • Robin Henry, Damien Ernst
In this work, we introduce Gym-ANM, a framework for designing reinforcement learning (RL) environments that model ANM tasks in electricity distribution networks.
no code implementations • 2 Mar 2021 • Decebal Constantin Mocanu, Elena Mocanu, Tiago Pinto, Selima Curci, Phuong H. Nguyen, Madeleine Gibescu, Damien Ernst, Zita A. Vale
A fundamental task for artificial intelligence is learning.
no code implementations • 22 Feb 2021 • Mathias Berger, David Radu, Ghislain Detienne, Thierry Deschuyteneer, Aurore Richel, Damien Ernst
The framework is leveraged to study the economics of carbon-neutral synthetic methane production from solar and wind energy in North Africa and its delivery to Northwestern European markets.
1 code implementation • 22 Dec 2020 • Pascal Leroy, Damien Ernst, Pierre Geurts, Gilles Louppe, Jonathan Pisane, Matthia Sabatelli
This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings.
no code implementations • 15 Nov 2020 • David Radu, Mathias Berger, Antoine Dubois, Raphael Fonteneau, Hrvoje Pandzic, Yury Dvorkin, Quentin Louveaux, Damien Ernst
In addition, two variants of these siting schemes are provided, wherein the number of sites to be selected is specified on a country-by-country basis rather than Europe-wide.
no code implementations • 9 Sep 2020 • Miguel Manuel de Villena, Samy Aittahar, Sebastien Mathieu, Ioannis Boukas, Eric Vermeulen, Damien Ernst
Local electricity markets represent a way of supplementing traditional retailing contracts for end consumers -- among these markets, the renewable energy community has gained momentum over the last few years.
no code implementations • 10 Jun 2020 • Thibaut Théate, Sébastien Mathieu, Damien Ernst
In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period.
3 code implementations • 9 Jun 2020 • Nicolas Vecoven, Damien Ernst, Guillaume Drion
Standard gated cells share a layer internal state to store information at the network level, and long term memory is shaped by network-wide recurrent connection weights.
1 code implementation • 2 Jun 2020 • Adrien Bolland, Ioannis Boukas, Mathias Berger, Damien Ernst
We assess the performance of our algorithm in three environments concerned with the design and control of a mass-spring-damper system, a small-scale off-grid power system and a drone, respectively.
no code implementations • 13 Apr 2020 • Ioannis Boukas, Damien Ernst, Thibaut Théate, Adrien Bolland, Alexandre Huynen, Martin Buchwald, Christelle Wynants, Bertrand Cornélusse
In this paper, we propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market where exchanges occur through a centralized order book.
1 code implementation • 7 Apr 2020 • Thibaut Théate, Damien Ernst
This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets.
1 code implementation • 21 Dec 2018 • Nicolas Vecoven, Damien Ernst, Antoine Wehenkel, Guillaume Drion
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack.
no code implementations • 22 Sep 2017 • Vincent Francois-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau
This paper provides an analysis of the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data) in the context of reinforcement learning with partial observability.
no code implementations • 7 Dec 2015 • Vincent François-Lavet, Raphael Fonteneau, Damien Ernst
When the discount factor progressively increases up to its final value, we empirically show that it is possible to significantly reduce the number of learning steps.
no code implementations • 14 Sep 2015 • Michael Castronovo, Damien Ernst, Adrien Couetoux, Raphael Fonteneau
In order to enable the comparison of non-anytime algorithms, our methodology also includes a detailed analysis of the computation time requirement of each algorithm.
1 code implementation • 30 Jun 2014 • Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data.
no code implementations • 12 Mar 2013 • Aivar Sootla, Natalja Strelkowa, Damien Ernst, Mauricio Barahona, Guy-Bart Stan
In this paper, we consider the problem of optimal exogenous control of gene regulatory networks.
no code implementations • 22 Jul 2012 • Sebastien Bubeck, Damien Ernst, Aurelien Garivier
We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice.