| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2020 | 169 | 2020 |
| A Geometric Perspective on Optimal Representations for Reinforcement Learning M Bellemare, W Dabney, R Dadashi, A Ali Taiga, PS Castro, N Le Roux, ... Neural Information Processing Systems (NeurIPS), 2019 | 79 | 2019 |
| Primal Wasserstein Imitation Learning R Dadashi, L Hussenot, M Geist, O Pietquin International Conference on Learning Representations (ICLR), 2021 | 71 | 2021 |
| Statistics and Samples in Distributional Reinforcement Learning M Rowland, R Dadashi, S Kumar, R Munos, MG Bellemare, W Dabney International Conference on Machine Learning (ICML), 2019 | 62 | 2019 |
| The Value-Improvement Path: Towards Better Representations for Reinforcement Learning W Dabney, A Barreto, M Rowland, R Dadashi, J Quan, MG Bellemare, ... AAAI Conference on Artificial Intelligence, 2021 | 43 | 2021 |
| The Value Function Polytope in Reinforcement Learning R Dadashi, AA Taïga, NL Roux, D Schuurmans, MG Bellemare International Conference on Machine Learning (ICML), 2019 | 36 | 2019 |
| What Matters for Adversarial Imitation Learning? M Orsini, A Raichuk, L Hussenot, D Vincent, R Dadashi, S Girgin, M Geist, ... Neural Information Processing Systems (NeurIPS), 2021 | 35 | 2021 |
| Offline Reinforcement Learning with Pseudometric Learning R Dadashi, S Rezaeifar, N Vieillard, L Hussenot, O Pietquin, M Geist International Conference on Machine Learning (ICML), 2021 | 24 | 2021 |
| Offline Reinforcement Learning as Anti-Exploration S Rezaeifar*, R Dadashi*, N Vieillard, L Hussenot, O Bachem, O Pietquin, ... AAAI Conference on Artificial Intelligence, 2022 | 17 | 2022 |
| Hyperparameter Selection for Imitation Learning L Hussenot, M Andrychowicz, D Vincent, R Dadashi, A Raichuk, ... International Conference on Machine Learning (ICML), 2021 | 11 | 2021 |
| Show me the Way: Intrinsic Motivation from Demonstrations L Hussenot, R Dadashi, M Geist, O Pietquin International Conference on Autonomous Agents and Multiagent Systems (AAMAS …, 2020 | 8 | 2020 |
| Continuous Control with Action Quantization from Demonstrations R Dadashi*, L Hussenot*, D Vincent, S Girgin, A Raichuk, M Geist, ... International Conference on Machine Learning (ICML), 2022 | 4 | 2022 |
| Generalized Policy Updates for Policy Optimization S Kumar, Z Ahmed, R Dadashi, D Schuurmans, MG Bellemare NeurIPS 2019 Optimization Foundations for Reinforcement Learning Workshop, 2019 | 3 | 2019 |
| Learning Energy Networks with Generalized Fenchel-Young Losses M Blondel, F Llinares-López, R Dadashi, L Hussenot, M Geist Neural Information Processing Systems (NeurIPS), 2022 | 1 | 2022 |
| Get Back Here: Robust Imitation by Return-to-Distribution Planning G Cideron, B Tabanpour, S Curi, S Girgin, L Hussenot, G Dulac-Arnold, ... arXiv preprint arXiv:2305.01400, 2023 | | 2023 |
Matthieu GeistGoogle Research, Brain Team (on leave of Professor, Université de Lorraine)Verified email at univ-lorraine.fr
