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Lambrechts Gaspard

Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids

Montefiore Institute

See author's contact details
Main Referenced Co-authors
Ernst, Damien  (9)
Bolland, Adrien  (5)
Close, Pierre  (2)
Geris, Liesbet  (2)
Joiret, Marc  (2)
Main Referenced Keywords
Asymmetric Learning (4); POMDP (4); Reinforcement Learning (4); RL (4); Partial Observability (3);
Main Referenced Unit & Research Centers
GIGA In silico medecine-Biomechanics Research Unit - ULiège (1)
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège (1)
Main Referenced Disciplines
Computer science (14)
Biochemistry, biophysics & molecular biology (2)
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others (2)
Personal website

Publications (total 14)

The most downloaded
379 downloads
Lambrechts, G. (2025). Reinforcement Learning in Partially Observable Markov Decision Processes: Learning to Remember the Past by Learning to Predict the Future [Doctoral thesis, ULiège - University of Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/328700 https://hdl.handle.net/2268/328700

The most cited

5 citations (OpenAlex)

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 https://hdl.handle.net/2268/302173

The most significant

Lambrechts, G., Ernst, D., & Mahajan, A. (2025). A Theoretical Justification for Asymmetric Actor-Critic Algorithms. Proceedings of Machine Learning Research.
Peer reviewed

Lambrechts, G., Bolland, A., & Ernst, D. (2024). Informed POMDP: Leveraging Additional Information in Model-Based RL. Reinforcement Learning Journal.
Peer reviewed

Lambrechts, G.* , Claes, Y.* , Geurts, P., & Ernst, D. (July 2024). Parallelizing Autoregressive Generation with Variational State Space Models [Paper presentation]. ICML Workshop on Next Generation of Sequence Modeling Architectures, Vienne, Austria.
Peer reviewed
* These authors have contributed equally to this work.

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. (2022). Recurrent networks, hidden states and beliefs in partially observable environments. Transactions on Machine Learning Research.
Peer Reviewed verified by ORBi


Lambrechts, G. (2025). Partial Observability and Asymmetric Observability [Paper presentation]. BeNeRL Workshop, Eindhoven, Netherlands.

Lambrechts, G., Ernst, D., & Mahajan, A. (2025). A Theoretical Justification for Asymmetric Actor-Critic Algorithms. Proceedings of Machine Learning Research.
Peer reviewed

Lambrechts, G. (2025). Reinforcement Learning in Partially Observable Markov Decision Processes: Learning to Remember the Past by Learning to Predict the Future [Doctoral thesis, ULiège - University of Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/328700

Lambrechts, G., Bolland, A., & Ernst, D. (2024). Informed POMDP: Leveraging Additional Information in Model-Based RL. Reinforcement Learning Journal.
Peer reviewed

Lambrechts, G.* , Claes, Y.* , Geurts, P., & Ernst, D. (July 2024). Parallelizing Autoregressive Generation with Variational State Space Models [Paper presentation]. ICML Workshop on Next Generation of Sequence Modeling Architectures, Vienne, Austria.
Peer reviewed
* These authors have contributed equally to this work.

Louette, A., Lambrechts, G., Ernst, D., Pirard, E., & Dislaire, G. (2024). Reinforcement learning to improve delta robot throws for sorting scrap metal. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/319923.

Bolland, A., Lambrechts, G., & Ernst, D. (2024). Off-Policy Maximum Entropy RL with Future State and Action Visitation Measures. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/325301.

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.

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 verified by ORBi

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