  | Lambrechts, G. (2023). Learning to Remember the Past by Learning to Predict the Future [Paper presentation]. VUB Reinforcement Learning Talks. |
 | Lambrechts, G.* , De Geeter, F.* , Vecoven, N.* , Ernst, D., & Drion, G. (August 2023). Warming up recurrent neural networks to maximise reachable multistability greatly improves learning. Neural Networks, 166, 645-669. doi:10.1016/j.neunet.2023.07.023  Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
 | Lambrechts, G., Bolland, A., & Ernst, D. (June 2023). Informed POMDP: Leveraging Additional Information in Model-Based RL [Paper presentation]. ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, Honolulu, United States - Hawaii. Peer reviewed |
 | Lambrechts, G., Bolland, A., & Ernst, D. (September 2022). Belief states of POMDPs and internal states of recurrent RL agents: an empirical analysis of their mutual information [Paper presentation]. European Workshop on Reinforcement Learning, Milan, Italy. Peer reviewed |
 | Lambrechts, G., Bolland, A., & Ernst, D. (2022). Recurrent networks, hidden states and beliefs in partially observable environments. Transactions on Machine Learning Research. Peer reviewed |
 | Bolland, A., Lambrechts, G., & Ernst, D. (2024). Behind the Myth of Exploration in Policy Gradients. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/312658. |
| Lambrechts, G. (2023). Learning to Remember the Past by Learning to Predict the Future [Paper presentation]. VUB Reinforcement Learning Talks. |
| Lambrechts, G.* , De Geeter, F.* , Vecoven, N.* , Ernst, D., & Drion, G. (August 2023). Warming up recurrent neural networks to maximise reachable multistability greatly improves learning. Neural Networks, 166, 645-669. doi:10.1016/j.neunet.2023.07.023 Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
| Lambrechts, G., Bolland, A., & Ernst, D. (June 2023). Informed POMDP: Leveraging Additional Information in Model-Based RL [Paper presentation]. ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, Honolulu, United States - Hawaii. Peer reviewed |
 | Joiret, M., Leclercq, M., Lambrechts, G., Rapino, F., Close, P., Louppe, G., & Geris, L. (06 April 2023). Cracking the genetic code with neural networks. Frontiers in Artificial Intelligence, 6. doi:10.3389/frai.2023.1128153 Peer Reviewed verified by ORBi |
 | Joiret, M., Leclercq, M., Lambrechts, G., Rapino, F., Close, P., Louppe, G., & Geris, L. (2023). Cracking the genetic code with neural networks (poster) [Paper presentation]. Neural networks: real versus man-made, Liège, Belgium. |
| Lambrechts, G., Bolland, A., & Ernst, D. (September 2022). Belief states of POMDPs and internal states of recurrent RL agents: an empirical analysis of their mutual information [Paper presentation]. European Workshop on Reinforcement Learning, Milan, Italy. Peer reviewed |
| Lambrechts, G., Bolland, A., & Ernst, D. (2022). Recurrent networks, hidden states and beliefs in partially observable environments. Transactions on Machine Learning Research. Peer reviewed |
