Robert Dadashi
Robert Dadashi
Google DeepMind
Verified email at - Homepage
Cited by
Cited by
Gemini: A family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
Gemma: Open Models Based on Gemini Research and Technology
G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ...
arXiv preprint arXiv:2403.08295, 2024
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
Primal Wasserstein Imitation Learning
R Dadashi, L Hussenot, M Geist, O Pietquin
International Conference on Learning Representations (ICLR), 2021
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
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
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
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
Offline Reinforcement Learning as Anti-Exploration
S Rezaeifar*, R Dadashi*, N Vieillard, L Hussenot, O Bachem, O Pietquin, ...
AAAI Conference on Artificial Intelligence, 2022
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
P Roit, J Ferret, L Shani, R Aharoni, G Cideron, R Dadashi, M Geist, ...
Annual Meeting of the Association for Computational Linguistics (ACL), 2023
The Value Function Polytope in Reinforcement Learning
R Dadashi, AA Taïga, NL Roux, D Schuurmans, MG Bellemare
International Conference on Machine Learning (ICML), 2019
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
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
WARM: On the Benefits of Weight Averaged Reward Models
A Ramé, N Vieillard, L Hussenot, R Dadashi, G Cideron, O Bachem, ...
arXiv preprint arXiv:2401.12187, 2024
Hyperparameter Selection for Imitation Learning
L Hussenot, M Andrychowicz, D Vincent, R Dadashi, A Raichuk, ...
International Conference on Machine Learning (ICML), 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
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
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
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
A Botev, S De, SL Smith, A Fernando, GC Muraru, R Haroun, L Berrada, ...
arXiv preprint arXiv:2404.07839, 2024
Generalized policy updates for policy optimization
S Kumar, R Dadashi, Z Ahmed, D Schuurmans, MG Bellemare
NeurIPS 2019 Optimization Foundations for Reinforcement Learning Workshop, 2019
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