| Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning E Lecarpentier, E Rachelson Advances in Neural Information Processing Systems (NeurIPS) 32, 7216--7225, 2019 | 88 | 2019 |
| Lipschitz lifelong reinforcement learning E Lecarpentier, D Abel, K Asadi, Y Jinnai, E Rachelson, ML Littman AAAI Conference on Artificial Intelligence, AAAI 2021, 2020 | 36 | 2020 |
| Open loop execution of tree-search algorithms E Lecarpentier, G Infantes, C Lesire, E Rachelson International Joint Conference on Artificial Intelligence, IJCAI 2018, 2362 …, 2018 | 16* | 2018 |
| Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring E Lecarpentier, S Rapp, M Melo, E Rachelson arXiv preprint arXiv:1707.05668, 2017 | 4 | 2017 |
| Reinforcement Learning in Non-Stationary Environments E Lecarpentier Institut Supérieur de l'Aéronautique et de l'Espace (ISAE), 2020 | 3 | 2020 |
| DARTS-PRIME: Regularization and scheduling improve constrained optimization in differentiable NAS K Maile, E Lecarpentier, H Luga, DG Wilson arXiv preprint arXiv:2106.11655, 2021 | 2 | 2021 |
| LUCIE: an evaluation and selection method for stochastic problems E Lecarpentier, P Templier, E Rachelson, DG Wilson Proceedings of the Genetic and Evolutionary Computation Conference, 730-738, 2022 | 1 | 2022 |
| On constrained optimization in differentiable neural architecture search K Maile, E Lecarpentier, H Luga, DG Wilson CoRR, 2021 | 1 | 2021 |
| LUCIE: An Evaluation and Selection Method for Stochastic Problems–Appendix E LECARPENTIER, P TEMPLIER, E RACHELSON, DG WILSON | | 2022 |
| Processus décisionnels de Markov non-stationnaires une approche pire-cas utilisant l’apprentissage par renforcement basé modèle E Lecarpentier, E Rachelson JFPDA, 105, 0 | | |
Emmanuel RachelsonISAE-SUPAERO在 isae-supaero.fr 的电子邮件经过验证
