| Informed POMDP: Leveraging Additional Information in Model-Based RL G Lambrechts, A Bolland, D Ernst Reinforcement Learning Conference, 2024 | 8 | 2024 |
| Recurrent networks, hidden states and beliefs in partially observable environments G Lambrechts, A Bolland, D Ernst Transactions on Machine Learning Research, 2022 | 7 | 2022 |
| Warming up recurrent neural networks to maximise reachable multistability greatly improves learning G Lambrechts, F De Geeter, N Vecoven, D Ernst, G Drion Neural Networks 166, 645-669, 2023 | 3 | 2023 |
| Parallelizing Autoregressive Generation with Variational State Space Models G Lambrechts, Y Claes, P Geurts, D Ernst ICML 2024 Workshop on the Next Generation of Sequence Modeling Architectures, 2024 | | 2024 |
| Reinforcement Learning to improve delta robot throws for sorting scrap metal A Louette, G Lambrechts, D Ernst, E Pirard, G Disclaire arXiv preprint arXiv:2406.13453, 2024 | | 2024 |
| Behind the Myth of Exploration in Policy Gradients A Bolland, G Lambrechts, D Ernst arXiv preprint arXiv:2402.00162, 2024 | | 2024 |
| Learning to Remember the Past by Learning to Predict the Future G Lambrechts VUB Reinforcement Learning Talks, 2023 | | 2023 |
| 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 |
Damien ErnstProfessor of Electrical Engineering and Computer Science, ULiège在 uliege.be 的电子邮件经过验证
