| Deep reinforcement learning at the edge of the statistical precipice R Agarwal, M Schwarzer, PS Castro, AC Courville, M Bellemare Advances in neural information processing systems 34, 29304-29320, 2021 | 546 | 2021 |
| Rigging the lottery: Making all tickets winners U Evci, T Gale, J Menick, PS Castro, E Elsen International conference on machine learning, 2943-2952, 2020 | 524 | 2020 |
| Autonomous navigation of stratospheric balloons using reinforcement learning MG Bellemare, S Candido, PS Castro, J Gong, MC Machado, S Moitra, ... Nature 588 (7836), 77-82, 2020 | 346 | 2020 |
| From taxi GPS traces to social and community dynamics: A survey PS Castro, D Zhang, C Chen, S Li, G Pan ACM Computing Surveys (CSUR) 46 (2), 1-34, 2013 | 341 | 2013 |
| Urban traffic modelling and prediction using large scale taxi GPS traces PS Castro, D Zhang, S Li International Conference on Pervasive Computing, 57-72, 2012 | 336 | 2012 |
| Dopamine: A research framework for deep reinforcement learning PS Castro, S Moitra, C Gelada, S Kumar, MG Bellemare arXiv preprint arXiv:1812.06110, 2018 | 287 | 2018 |
| iBOAT: Isolation-based online anomalous trajectory detection C Chen, D Zhang, PS Castro, N Li, L Sun, S Li, Z Wang IEEE Transactions on Intelligent Transportation Systems 14 (2), 806-818, 2013 | 217 | 2013 |
| Contrastive behavioral similarity embeddings for generalization in reinforcement learning R Agarwal, MC Machado, PS Castro, MG Bellemare arXiv preprint arXiv:2101.05265, 2021 | 186 | 2021 |
| TF-Agents: A library for reinforcement learning in tensorflow S Guadarrama, A Korattikara, O Ramirez, P Castro, E Holly, S Fishman, ... GitHub repository, 2018 | 169 | 2018 |
| Real-time detection of anomalous taxi trajectories from GPS traces C Chen, D Zhang, P Samuel Castro, N Li, L Sun, S Li International Conference on Mobile and Ubiquitous Systems: Computing …, 2011 | 138 | 2011 |
| Scalable methods for computing state similarity in deterministic markov decision processes PS Castro Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 10069 …, 2020 | 131 | 2020 |
| Revisiting rainbow: Promoting more insightful and inclusive deep reinforcement learning research JSO Ceron, PS Castro International Conference on Machine Learning, 1373-1383, 2021 | 116* | 2021 |
| A geometric perspective on optimal representations for reinforcement learning M Bellemare, W Dabney, R Dadashi, A Ali Taiga, PS Castro, N Le Roux, ... Advances in neural information processing systems 32, 2019 | 100 | 2019 |
| Methods for computing state similarity in Markov decision processes N Ferns, PS Castro, D Precup, P Panangaden arXiv preprint arXiv:1206.6836, 2012 | 95 | 2012 |
| A comparative analysis of expected and distributional reinforcement learning C Lyle, MG Bellemare, PS Castro Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4504-4511, 2019 | 88 | 2019 |
| Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks M Chevalier-Boisvert, B Dai, M Towers, R Perez-Vicente, L Willems, ... Advances in Neural Information Processing Systems 36, 2024 | 74 | 2024 |
| Using bisimulation for policy transfer in MDPs P Castro, D Precup Proceedings of the AAAI conference on artificial intelligence 24 (1), 1065-1070, 2010 | 65 | 2010 |
| An atari model zoo for analyzing, visualizing, and comparing deep reinforcement learning agents FP Such, V Madhavan, R Liu, R Wang, PS Castro, Y Li, J Zhi, L Schubert, ... arXiv preprint arXiv:1812.07069, 2018 | 60 | 2018 |
| Reincarnating reinforcement learning: Reusing prior computation to accelerate progress R Agarwal, M Schwarzer, PS Castro, AC Courville, M Bellemare Advances in Neural Information Processing Systems 35, 28955-28971, 2022 | 53* | 2022 |
| Mico: Improved representations via sampling-based state similarity for markov decision processes PS Castro, T Kastner, P Panangaden, M Rowland Advances in Neural Information Processing Systems 34, 30113-30126, 2021 | 51 | 2021 |
Marc G. BellemareGoogle Brain在 google.com 的电子邮件经过验证
