Search Results for author: Thibaut Théate

Found 6 papers, 3 papers with code

Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis

no code implementations27 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.

Decision Making

Risk-Sensitive Policy with Distributional Reinforcement Learning

1 code implementation30 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.

Decision Making Distributional Reinforcement Learning +2

Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks

1 code implementation6 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

An Artificial Intelligence Solution for Electricity Procurement in Forward Markets

no code implementations10 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.

A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding

no code implementations13 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.

Decision Making reinforcement-learning +1

An Application of Deep Reinforcement Learning to Algorithmic Trading

1 code implementation7 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.

Algorithmic Trading reinforcement-learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.