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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 | 364 | 2020 |
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 | 239 | 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 | 213 | 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 | 164 | 2021 |
Seed rl: Scalable and efficient deep-rl with accelerated central inference L Espeholt, R Marinier, P Stanczyk, K Wang, M Michalski arXiv preprint arXiv:1910.06591, 2019 | 138 | 2019 |
Factually consistent summarization via reinforcement learning with textual entailment feedback P Roit, J Ferret, L Shani, R Aharoni, G Cideron, R Dadashi, M Geist, ... arXiv preprint arXiv:2306.00186, 2023 | 42 | 2023 |
Gkd: Generalized knowledge distillation for auto-regressive sequence models R Agarwal, N Vieillard, P Stanczyk, S Ramos, M Geist, O Bachem arXiv preprint arXiv:2306.13649, 2023 | 37 | 2023 |
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 | 30 | 2020 |
Launchpad: A programming model for distributed machine learning research F Yang, G Barth-Maron, P Stańczyk, M Hoffman, S Liu, M Kroiss, A Pope, ... arXiv preprint arXiv:2106.04516, 2021 | 21 | 2021 |
Perfect Matching for Biconnected Cubic Graphs in O(n log2 n) Time K Diks, P Stanczyk SOFSEM 2010: Theory and Practice of Computer Science: 36th Conference on …, 2010 | 16 | 2010 |
Rlds: an ecosystem to generate, share and use datasets in reinforcement learning S Ramos, S Girgin, L Hussenot, D Vincent, H Yakubovich, D Toyama, ... arXiv preprint arXiv:2111.02767, 2021 | 12 | 2021 |
On-policy distillation of language models: Learning from self-generated mistakes R Agarwal, N Vieillard, Y Zhou, P Stanczyk, SR Garea, M Geist, ... The Twelfth International Conference on Learning Representations, 2024 | 11 | 2024 |
Generalized knowledge distillation for auto-regressive language models R Agarwal, N Vieillard, Y Zhou, P Stanczyk, S Ramos, M Geist, O Bachem The Twelfth International Conference on Learning Representations, 2024 | 6 | 2024 |
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Google research football K Kurach, A Raichuk, P Stanczyk, M Zajac, O Bachem, L Espeholt, ... A” Novel Reinforcement Learning Environment”, CoRR, 2019 | 5 | 2019 |
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SIO .NET Plug&Play Contest System M Michalski, M Kosieradzki, W Rygielski, P Stańczyk, K Ciebiera, K Diks Perspectives on Computer Science Competitions for (High School) Students, 2005 | 3 | 2005 |
Reinforcement learning with centralized inference and training L Espeholt, K Wang, MM Michalski, PM Stanczyk, R Marinier US Patent App. 17/764,066, 2022 | 1 | 2022 |
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning H Yakubovich, D Toyama, A Gergely, P Stanczyk | | |