| A deep reinforcement learning framework for continuous intraday market bidding I Boukas, D Ernst, T Théate, A Bolland, A Huynen, M Buchwald, ... Machine Learning 110 (9), 2335-2387, 2021 | 55 | 2021 |
| Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent A Bolland, I Boukas, M Berger, D Ernst Journal of Artificial Intelligence Research 73, 117-171, 2022 | 8* | 2022 |
| Recurrent networks, hidden states and beliefs in partially observable environments G Lambrechts, A Bolland, D Ernst Transactions on Machine Learning Research, 2022 | 7 | 2022 |
| Informed POMDP: Leveraging Additional Information in Model-Based RL G Lambrechts, A Bolland, D Ernst ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023 | 5 | 2023 |
| Graph-Based Optimization Modeling Language: A Tutorial M Berger, A Bolland, B Miftari, H Djelassi, D Ernst | 4 | 2021 |
| Distributional reinforcement learning with unconstrained monotonic neural networks T Théate, A Wehenkel, A Bolland, G Louppe, D Ernst Neurocomputing 534, 199-219, 2023 | 3 | 2023 |
| Policy Gradient Algorithms Implicitly Optimize by Continuation A Bolland, G Louppe, D Ernst Transactions on Machine Learning Research, 2023 | 2 | 2023 |
| Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms S Aittahar, A Bolland, G Derval, D Ernst arXiv preprint arXiv:2401.16321, 2024 | 1 | 2024 |
| Reinforcement Learning for Joint Design and Control of Battery-PV Systems M Cauz, A Bolland, B Miftari, L Perret, C Ballif, N Wyrsch Proceedings of ECOS 2023: The 36th International Conference on Efficiency …, 2023 | 1 | 2023 |
| Behind the Myth of Exploration in Policy Gradients A Bolland, G Lambrechts, D Ernst arXiv preprint arXiv:2402.00162, 2024 | | 2024 |
| Belief states of POMDPs and internal states of recurrent RL agents: an empirical analysis of their mutual information G Lambrechts, A Bolland, D Ernst European Workshop on Reinforcement Learning, 2022 | | 2022 |
| Master's Thesis: Deep Reinforcement Learning with Applications to the Renewable Energy Transition A Bolland Université de Liège, Liège, Belgique, 2020 | | 2020 |
Damien ErnstProfessor of Electrical Engineering and Computer Science, ULiège - University of LiègeVerified email at ulg.ac.be
