Learning optimal environments using projected stochastic gradient ascent. - Adrien Bolland; Ioannis Boukas; François Cornet; Mathias Berger; Damien Ernst (CoRR, 2020)

A deep reinforcement learning framework for continuous intraday market bidding. - Ioannis Boukas; Damien Ernst; Thibaut Théate; Adrien Bolland; Alexandre Huynen; Martin Buchwald; Christelle Wynants; Bertrand Cornélusse (Mach. Learn., 2021)

Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent. - Adrien Bolland; Ioannis Boukas; Mathias Berger; Damien Ernst (J. Artif. Intell. Res., 2022)

Recurrent networks, hidden states and beliefs in partially observable environments. - Gaspard Lambrechts; Adrien Bolland; Damien Ernst (Trans. Mach. Learn. Res., 2022)

Distributional reinforcement learning with unconstrained monotonic neural networks. - Thibaut Théate; Antoine Wehenkel; Adrien Bolland; Gilles Louppe; Damien Ernst (Neurocomputing, 2023)

Policy Gradient Algorithms Implicitly Optimize by Continuation. - Adrien Bolland; Gilles Louppe; Damien Ernst (Trans. Mach. Learn. Res., 2023)

Informed POMDP: Leveraging Additional Information in Model-Based RL. - Gaspard Lambrechts; Adrien Bolland; Damien Ernst (RLJ, 2024)

Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms. - Samy Aittahar; Adrien Bolland; Guillaume Derval; Damien Ernst (CoRR, 2024)

Behind the Myth of Exploration in Policy Gradients. - Adrien Bolland; Gaspard Lambrechts; Damien Ernst (CoRR, 2024)

Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems. - Marine Cauz; Adrien Bolland; Nicolas Wyrsch; Christophe Ballif (CoRR, 2024)

Costs Estimation in Unit Commitment Problems using Simulation-Based Inference. - Matthias Pirlet; Adrien Bolland; Gilles Louppe; Damien Ernst (CoRR, 2024)

Off-Policy Maximum Entropy RL with Future State and Action Visitation Measures. - Adrien Bolland; Gaspard Lambrechts; Damien Ernst (CoRR, 2024)