222 citations · 242 across the 6 of their papers we have counts for
6 papers
Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis
Thibaut Théate, Antonio Sutera, Damien Ernst
The energy transition is expected to significantly increase the share of renewable energy sources whose production is intermittent in the electricity mix. Apart from key benefits,…
Risk-Sensitive Policy with Distributional Reinforcement Learning
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. Nevertheles…
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
Thibaut Théate, Antoine Wehenkel, Adrien Bolland +2
The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectat…
An Artificial Intelligence Solution for Electricity Procurement in Forward Markets
Thibaut Théate, Sébastien Mathieu, Damien Ernst
Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term elect…
A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding
Ioannis Boukas, Damien Ernst, Thibaut Théate +5
The large integration of variable energy resources is expected to shift a large part of the energy exchanges closer to real-time, where more accurate forecasts are available. In th…
An Application of Deep Reinforcement Learning to Algorithmic Trading
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 tradin…