| Google research football: A novel reinforcement learning environment K Kurach, A Raichuk, P Stańczyk, M Zając, O Bachem, L Espeholt, ... Proceedings of the AAAI conference on artificial intelligence 34 (04), 4501-4510, 2020 | 430 | 2020 |
| Episodic curiosity through reachability N Savinov, A Raichuk, R Marinier, D Vincent, M Pollefeys, T Lillicrap, ... arXiv preprint arXiv:1810.02274, 2018 | 349 | 2018 |
| Brax--a differentiable physics engine for large scale rigid body simulation CD Freeman, E Frey, A Raichuk, S Girgin, I Mordatch, O Bachem arXiv preprint arXiv:2106.13281, 2021 | 270 | 2021 |
| 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 | 270 | 2020 |
| What matters in on-policy reinforcement learning? a large-scale empirical study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... arXiv preprint arXiv:2006.05990, 2020 | 255 | 2020 |
| What matters for on-policy deep actor-critic methods? a large-scale study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... International conference on learning representations, 2021 | 200 | 2021 |
| What matters for adversarial imitation learning? M Orsini, A Raichuk, L Hussenot, D Vincent, R Dadashi, S Girgin, M Geist, ... Advances in Neural Information Processing Systems 34, 14656-14668, 2021 | 82 | 2021 |
| Brax-a differentiable physics engine for large scale rigid body simulation, 2021 CD Freeman, E Frey, A Raichuk, S Girgin, I Mordatch, O Bachem URL http://github. com/google/brax 6, 2021 | 72 | 2021 |
| What matters in on-policy reinforcement learning M Andrychowicz, A Raichuk, P Stanczyk, M Orsini, S Girgin, R Marinier, ... A large-scale empirical study. CoRR, abs/2006.05990 3, 2020 | 37 | 2020 |
| Continuous control with action quantization from demonstrations R Dadashi, L Hussenot, D Vincent, S Girgin, A Raichuk, M Geist, ... arXiv preprint arXiv:2110.10149, 2021 | 35 | 2021 |
| Hyperparameter selection for imitation learning L Hussenot, M Andrychowicz, D Vincent, R Dadashi, A Raichuk, S Ramos, ... International Conference on Machine Learning, 4511-4522, 2021 | 21 | 2021 |
| Braxlines: Fast and interactive toolkit for rl-driven behavior engineering beyond reward maximization SS Gu, M Diaz, DC Freeman, H Furuta, SKS Ghasemipour, A Raichuk, ... arXiv preprint arXiv:2110.04686, 2021 | 15 | 2021 |
| Agent-centric representations for multi-agent reinforcement learning W Shang, L Espeholt, A Raichuk, T Salimans arXiv preprint arXiv:2104.09402, 2021 | 12 | 2021 |
| Implicitly regularized rl with implicit q-values N Vieillard, M Andrychowicz, A Raichuk, O Pietquin, M Geist arXiv preprint arXiv:2108.07041, 2021 | 10 | 2021 |
| Sta nczyk M Andrychowicz, A Raichuk P, 0 | 10 | |
| vec2text with round-trip translations G Cideron, S Girgin, A Raichuk, O Pietquin, O Bachem, L Hussenot arXiv preprint arXiv:2209.06792, 2022 | 4 | 2022 |
| Planted: a dataset for planted forest identification from multi-satellite time series LM Pazos-Outón, CN Vasconcelos, A Raichuk, A Arnab, D Morris, ... IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium …, 2024 | 1 | 2024 |
| Global drivers of forest loss at 1 km resolution M Sims, R Stanimirova, A Raichuk, M Neumann, J Richter, F Follett, ... EarthArXiv, 2024 | | 2024 |
| State-dependent action space quantization R Dadashi-Tazehozi, OC Pietquin, LH Desenonges, MF Geist, A Raichuk, ... US Patent App. 17/947,985, 2023 | | 2023 |
| Global Drivers of Forest Loss at 1 km Resolution R Stanimirova, M Sims, A Raichuk, M Neumann, J Richter, J MacCarthy, ... AGU24, 0 | | |
