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2020 – today
- 2024
[j17]Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni:
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning. Trans. Mach. Learn. Res. 2024 (2024)- [c112]Kai Cui
, Gökçe Dayanikli, Mathieu Laurière, Matthieu Geist, Olivier Pietquin, Heinz Koeppl:
Learning Discrete-Time Major-Minor Mean Field Games. AAAI 2024: 9616-9625 - [c111]Zida Wu, Mathieu Laurière, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta:
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning. AAMAS 2024: 2561-2563 - [c110]Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem:
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. ICLR 2024 - [c109]Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach:
Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View. ICLR 2024 - [c108]Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian McWilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Léonard Hussenot, Neil Zeghidour, Andrea Agostinelli:
MusicRL: Aligning Music Generation to Human Preferences. ICML 2024 - [c107]Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. ICML 2024 - [i81]Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach:
Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View. CoRR abs/2401.11237 (2024) - [i80]Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian McWilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Léonard Hussenot, Neil Zeghidour, Andrea Agostinelli:
MusicRL: Aligning Music Generation to Human Preferences. CoRR abs/2402.04229 (2024) - [i79]Zida Wu, Mathieu Laurière, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta:
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning. CoRR abs/2403.03552 (2024) - [i78]Andrej Orsula, Matthieu Geist, Miguel A. Olivares-Méndez, Carol Chamorro-Martínez:
Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space. CoRR abs/2405.01134 (2024) - [i77]Eugene Choi, Arash Ahmadian, Matthieu Geist, Olivier Pietquin, Mohammad Gheshlaghi Azar:
Self-Improving Robust Preference Optimization. CoRR abs/2406.01660 (2024) - [i76]Pierre Clavier, Emmanuel Rachelson, Erwan Le Pennec, Matthieu Geist:
Bootstrapping Expectiles in Reinforcement Learning. CoRR abs/2406.04081 (2024) - [i75]Adil Zouitine, David Bertoin, Pierre Clavier, Matthieu Geist, Emmanuel Rachelson:
Time-Constrained Robust MDPs. CoRR abs/2406.08395 (2024) - [i74]Adil Zouitine, David Bertoin, Pierre Clavier, Matthieu Geist, Emmanuel Rachelson:
RRLS : Robust Reinforcement Learning Suite. CoRR abs/2406.08406 (2024) - [i73]Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist:
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. CoRR abs/2406.19185 (2024) - [i72]Nathan Grinsztajn, Yannis Flet-Berliac, Mohammad Gheshlaghi Azar, Florian Strub, Bill Wu, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Olivier Pietquin, Matthieu Geist:
Averaging log-likelihoods in direct alignment. CoRR abs/2406.19188 (2024) - 2023
- [c106]Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor:
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. ACL (1) 2023: 6252-6272 - [c105]Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon:
Extreme Q-Learning: MaxEnt RL without Entropy. ICLR 2023 - [c104]Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov:
A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. ICML 2023: 9485-9507 - [c103]Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo:
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML 2023: 17135-17175 - [c102]Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He:
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. ICML 2023: 39722-39754 - [c101]Othmane-Latif Ouabi, Neil Zeghidour, Nico F. Declercq, Matthieu Geist, Cédric Pradalier:
Pose-graph SLAM Using Multi-order Ultrasonic Echoes and Beamforming for Long-range Inspection Robots. ICRA 2023: 10623-10629 - [c100]Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Y. Levy, Shie Mannor:
Policy Gradient for Rectangular Robust Markov Decision Processes. NeurIPS 2023 - [c99]Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist:
On Imitation in Mean-field Games. NeurIPS 2023 - [c98]Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Matthieu Geist, Yuejie Chi:
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model. NeurIPS 2023 - [c97]Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist:
Offline Reinforcement Learning with On-Policy Q-Function Regularization. ECML/PKDD (4) 2023: 455-471 - [i71]Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon:
Extreme Q-Learning: MaxEnt RL without Entropy. CoRR abs/2301.02328 (2023) - [i70]Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Levy, Shie Mannor:
Policy Gradient for s-Rectangular Robust Markov Decision Processes. CoRR abs/2301.13589 (2023) - [i69]Pierre Clavier, Erwan Le Pennec, Matthieu Geist:
Towards Minimax Optimality of Model-based Robust Reinforcement Learning. CoRR abs/2302.05372 (2023) - [i68]Esther Derman, Yevgeniy Men, Matthieu Geist, Shie Mannor:
Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization. CoRR abs/2303.06654 (2023) - [i67]Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi, Sertan Girgin, Léonard Hussenot, Gabriel Dulac-Arnold, Matthieu Geist, Olivier Pietquin, Robert Dadashi:
Get Back Here: Robust Imitation by Return-to-Distribution Planning. CoRR abs/2305.01400 (2023) - [i66]Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo:
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR abs/2305.13185 (2023) - [i65]Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Matthieu Geist, Yuejie Chi:
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model. CoRR abs/2305.16589 (2023) - [i64]Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor:
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. CoRR abs/2306.00186 (2023) - [i63]Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem:
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR abs/2306.13649 (2023) - [i62]Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist:
On Imitation in Mean-field Games. CoRR abs/2306.14799 (2023) - [i61]Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov:
A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning. CoRR abs/2307.12968 (2023) - [i60]Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist:
Offline Reinforcement Learning with On-Policy Q-Function Regularization. CoRR abs/2307.13824 (2023) - [i59]Matteo El Hariry, Antoine Richard, Vivek Muralidharan, Baris Can Yalçin, Matthieu Geist, Miguel A. Olivares-Méndez:
DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories. CoRR abs/2310.04266 (2023) - [i58]Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. CoRR abs/2312.00886 (2023) - [i57]Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni:
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning. CoRR abs/2312.01072 (2023) - [i56]Kai Cui, Gökçe Dayanikli, Mathieu Laurière, Matthieu Geist, Olivier Pietquin, Heinz Koeppl:
Learning Discrete-Time Major-Minor Mean Field Games. CoRR abs/2312.10787 (2023) - 2022
- [c96]Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning as Anti-exploration. AAAI 2022: 8106-8114 - [c95]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, Olivier Pietquin:
Generalization in Mean Field Games by Learning Master Policies. AAAI 2022: 9413-9421 - [c94]Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist:
Implicitly Regularized RL with Implicit Q-values. AISTATS 2022: 1380-1402 - [c93]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS 2022: 8619-8649 - [c92]Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin:
Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. AAMAS 2022: 489-497 - [c91]Alexis Jacq, Johan Ferret, Olivier Pietquin, Matthieu Geist:
Lazy-MDPs: Towards Interpretable RL by Learning When to Act. AAMAS 2022: 669-677 - [c90]Julien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin:
Scaling Mean Field Games by Online Mirror Descent. AAMAS 2022: 1028-1037 - [c89]Othmane-Latif Ouabi, Jiawei Yi, Neil Zeghidour, Nico F. Declercq, Matthieu Geist, Cédric Pradalier:
Polygonal Shapes Reconstruction from Acoustic Echoes Using a Mobile Sensor and Beamforming. EUSIPCO 2022: 1507-1511 - [c88]Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin:
Continuous Control with Action Quantization from Demonstrations. ICML 2022: 4537-4557 - [c87]Thibault Lahire, Matthieu Geist, Emmanuel Rachelson:
Large Batch Experience Replay. ICML 2022: 11790-11813 - [c86]Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Elie, Olivier Pietquin, Matthieu Geist:
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. ICML 2022: 12078-12095 - [c85]Othmane-Latif Ouabi, Ayoub Ridani, Pascal Pomarede, Neil Zeghidour, Nico F. Declercq, Matthieu Geist, Cédric Pradalier:
Combined Grid and Feature-based Mapping of Metal Structures with Ultrasonic Guided Waves. ICRA 2022: 5056-5062 - [c84]Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist:
Learning Energy Networks with Generalized Fenchel-Young Losses. NeurIPS 2022 - [i55]Alexis Jacq, Johan Ferret, Olivier Pietquin, Matthieu Geist:
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act. CoRR abs/2203.08542 (2022) - [i54]Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Élie, Olivier Pietquin, Matthieu Geist:
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. CoRR abs/2203.11973 (2022) - [i53]Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist:
Learning Energy Networks with Generalized Fenchel-Young Losses. CoRR abs/2205.09589 (2022) - [i52]Mathieu Laurière, Sarah Perrin, Matthieu Geist, Olivier Pietquin:
Learning Mean Field Games: A Survey. CoRR abs/2205.12944 (2022) - [i51]Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári:
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR abs/2205.14211 (2022) - [i50]Paul Muller, Romuald Elie, Mark Rowland, Mathieu Laurière, Julien Pérolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, Karl Tuyls:
Learning Correlated Equilibria in Mean-Field Games. CoRR abs/2208.10138 (2022) - [i49]Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem:
C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. CoRR abs/2211.03521 (2022) - [i48]Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He:
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. CoRR abs/2212.14449 (2022) - 2021
- [j16]Antoine Mahé, Antoine Richard, Stéphanie Aravecchia, Matthieu Geist, Cédric Pradalier:
Evaluation of Prioritized Deep System Identification on a Path Following Task. J. Intell. Robotic Syst. 101(4): 78 (2021) - [j15]Othmane-Latif Ouabi, Pascal Pomarede, Matthieu Geist, Nico F. Declercq, Cédric Pradalier:
A FastSLAM Approach Integrating Beamforming Maps for Ultrasound-Based Robotic Inspection of Metal Structures. IEEE Robotics Autom. Lett. 6(2): 2908-2913 (2021) - [j14]Antoine Richard, Stéphanie Aravecchia, Thomas Schillaci, Matthieu Geist, Cédric Pradalier:
How to Train Your HERON. IEEE Robotics Autom. Lett. 6(3): 5247-5252 (2021) - [c83]Johan Ferret, Olivier Pietquin, Matthieu Geist:
Self-Imitation Advantage Learning. AAMAS 2021: 501-509 - [c82]Léonard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin:
Show Me the Way: Intrinsic Motivation from Demonstrations. AAMAS 2021: 620-628 - [c81]Antoine Richard, Stéphanie Aravecchia, Matthieu Geist, Cédric Pradalier:
Learning Behaviors through Physics-driven Latent Imagination. CoRL 2021: 1190-1199 - [c80]Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem:
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. ICLR 2021 - [c79]Robert Dadashi, Léonard Hussenot, Matthieu Geist, Olivier Pietquin:
Primal Wasserstein Imitation Learning. ICLR 2021 - [c78]Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
Adversarially Guided Actor-Critic. ICLR 2021 - [c77]Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning with Pseudometric Learning. ICML 2021: 2307-2318 - [c76]Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Sabela Ramos, Nikola Momchev, Sertan Girgin, Raphaël Marinier, Lukasz Stafiniak, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin:
Hyperparameter Selection for Imitation Learning. ICML 2021: 4511-4522 - [c75]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin:
Mean Field Games Flock! The Reinforcement Learning Way. IJCAI 2021: 356-362 - [c74]Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. NeurIPS 2021: 1898-1911 - [c73]Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz:
What Matters for Adversarial Imitation Learning? NeurIPS 2021: 14656-14668 - [c72]Esther Derman, Matthieu Geist, Shie Mannor:
Twice regularized MDPs and the equivalence between robustness and regularization. NeurIPS 2021: 22274-22287 - [i47]Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
Adversarially Guided Actor-Critic. CoRR abs/2102.04376 (2021) - [i46]Antoine Richard, Stéphanie Aravecchia, Thomas Schillaci, Matthieu Geist, Cédric Pradalier:
How To Train Your HERON. CoRR abs/2102.10357 (2021) - [i45]Julien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin:
Scaling up Mean Field Games with Online Mirror Descent. CoRR abs/2103.00623 (2021) - [i44]Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning with Pseudometric Learning. CoRR abs/2103.01948 (2021) - [i43]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin:
Mean Field Games Flock! The Reinforcement Learning Way. CoRR abs/2105.07933 (2021) - [i42]Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphaël Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin:
Hyperparameter Selection for Imitation Learning. CoRR abs/2105.12034 (2021) - [i41]Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz:
What Matters for Adversarial Imitation Learning? CoRR abs/2106.00672 (2021) - [i40]Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin:
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint. CoRR abs/2106.03787 (2021) - [i39]Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist:
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. CoRR abs/2106.04480 (2021) - [i38]Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning as Anti-Exploration. CoRR abs/2106.06431 (2021) - [i37]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A functional mirror ascent view of policy gradient methods with function approximation. CoRR abs/2108.05828 (2021) - [i36]Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist:
Implicitly Regularized RL with Implicit Q-Values. CoRR abs/2108.07041 (2021) - [i35]Sarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, Olivier Pietquin:
Generalization in Mean Field Games by Learning Master Policies. CoRR abs/2109.09717 (2021) - [i34]Thibault Lahire, Matthieu Geist, Emmanuel Rachelson:
Large Batch Experience Replay. CoRR abs/2110.01528 (2021) - [i33]Esther Derman, Matthieu Geist, Shie Mannor:
Twice regularized MDPs and the equivalence between robustness and regularization. CoRR abs/2110.06267 (2021) - [i32]Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin:
Continuous Control with Action Quantization from Demonstrations. CoRR abs/2110.10149 (2021) - 2020
- [c71]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Deep Conservative Policy Iteration. AAAI 2020: 6070-6077 - [c70]Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin:
On the Convergence of Model Free Learning in Mean Field Games. AAAI 2020: 7143-7150 - [c69]Alexis Jacq, Julien Pérolat, Matthieu Geist, Olivier Pietquin:
Foolproof Cooperative Learning. ACML 2020: 401-416 - [c68]Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist:
Momentum in Reinforcement Learning. AISTATS 2020: 2529-2538 - [c67]Léonard Hussenot, Matthieu Geist, Olivier Pietquin:
CopyCAT: : Taking Control of Neural Policies with Constant Attacks. AAMAS 2020: 548-556 - [c66]Erinc Merdivan, Sten Hanke, Matthieu Geist:
Modified Actor-Critics. AAMAS 2020: 1925-1927 - [c65]Assia Benbihi, Stéphanie Arravechia, Matthieu Geist, Cédric Pradalier:
Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description. ICRA 2020: 3032-3038 - [c64]Johan Ferret, Raphaël Marinier, Matthieu Geist, Olivier Pietquin:
Self-Attentional Credit Assignment for Transfer in Reinforcement Learning. IJCAI 2020: 2655-2661 - [c63]Othmane-Latif Ouabi, Pascal Pomarede, Matthieu Geist, Nico F. Declercq, Cédric Pradalier:
Monte-Carlo Localization on Metal Plates Based on Ultrasonic Guided Waves. ISER 2020: 345-353 - [c62]Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin:
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. NeurIPS 2020 - [c61]Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist:
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. NeurIPS 2020 - [c60]Nino Vieillard, Olivier Pietquin, Matthieu Geist:
Munchausen Reinforcement Learning. NeurIPS 2020 - [c59]Antoine Richard, Lior Fine, Offer Rozenstein, Josef Tanny, Matthieu Geist, Cédric Pradalier:
Filling Gaps in Micro-meteorological Data. ECML/PKDD (5) 2020: 101-117 - [i31]Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist:
Leverage the Average: an Analysis of Regularization in RL. CoRR abs/2003.14089 (2020) - [i30]Daoming Lyu, Bo Liu, Matthieu Geist, Wen Dong, Saad Biaz, Qi Wang:
Stable and Efficient Policy Evaluation. CoRR abs/2006.03978 (2020) - [i29]Robert Dadashi, Léonard Hussenot, Matthieu Geist, Olivier Pietquin:
Primal Wasserstein Imitation Learning. CoRR abs/2006.04678 (2020) - [i28]