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.
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 • 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.
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.