| Stable-baselines3: Reliable reinforcement learning implementations A Raffin, A Hill, A Gleave, A Kanervisto, M Ernestus, N Dormann Journal of machine learning research 22 (268), 1-8, 2021 | 5510 | 2021 |
| Stable baselines A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ... | 1062 | 2018 |
| Adversarial policies: Attacking deep reinforcement learning A Gleave, M Dennis, C Wild, N Kant, S Levine, S Russell International Conference on Learning Representations, 2020 | 645 | 2020 |
| Firmament: Fast, centralized cluster scheduling at scale I Gog, M Schwarzkopf, A Gleave, RNM Watson, S Hand 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2016 | 358 | 2016 |
| Multi-agent risks from advanced AI L Hammond, A Chan, J Clifton, J Hoelscher-Obermaier, A Khan, ... arXiv preprint arXiv:2502.14143, 2025 | 273 | 2025 |
| imitation: Clean imitation learning implementations A Gleave, M Taufeeque, J Rocamonde, E Jenner, SH Wang, S Toyer, ... arXiv preprint arXiv:2211.11972, 2022 | 152* | 2022 |
| Adversarial Policies Beat Superhuman Go AIs TT Wang, A Gleave, T Tseng, N Belrose, J Miller, MD Dennis, Y Duan, ... arXiv preprint arXiv:2211.00241, 2022 | 122* | 2022 |
| Scaling trends for data poisoning in llms D Bowen, B Murphy, W Cai, D Khachaturov, A Gleave, K Pelrine Proceedings of the AAAI Conference on Artificial Intelligence 39 (26), 27206 …, 2025 | 109* | 2025 |
| Quantifying differences in reward functions A Gleave, M Dennis, S Legg, S Russell, J Leike International Conference on Learning Representations, 2021 | 108 | 2021 |
| Invariance in policy optimisation and partial identifiability in reward learning JMV Skalse, M Farrugia-Roberts, S Russell, A Abate, A Gleave International Conference on Machine Learning, 32033-32058, 2023 | 85 | 2023 |
| Inverse reinforcement learning for video games A Tucker, A Gleave, S Russell Deep Reinforcement Learning Workshop at NeurIPS, 2018 | 74 | 2018 |
| Multi-task maximum entropy inverse reinforcement learning A Gleave, O Habryka GoalsRL Workshop at ICML, 2018 | 59 | 2018 |
| Exploiting novel GPT-4 apis K Pelrine, M Taufeeque, M Zając, E McLean, A Gleave arXiv preprint arXiv:2312.14302, 2023 | 52 | 2023 |
| Understanding learned reward functions EJ Michaud, A Gleave, S Russell Deep Reinforcement Learning Workshop at NeurIPS, 2020 | 52 | 2020 |
| Uncertainty estimation for language reward models A Gleave, G Irving arXiv preprint arXiv:2203.07472, 2022 | 48 | 2022 |
| A primer on maximum causal entropy inverse reinforcement learning A Gleave, S Toyer arXiv preprint arXiv:2203.11409, 2022 | 45 | 2022 |
| Active inverse reward design S Mindermann, R Shah, A Gleave, D Hadfield-Menell GoalsRL Workshop at ICML, 2018 | 40 | 2018 |
| Scaling trends in language model robustness N Howe, I McKenzie, O Hollinsworth, M Zajac, T Tseng, A Tucker, ... arXiv preprint arXiv:2407.18213, 2024 | 33* | 2024 |
| On the fragility of learned reward functions L McKinney, Y Duan, D Krueger, A Gleave arXiv preprint arXiv:2301.03652, 2023 | 32 | 2023 |
| Planning in a recurrent neural network that plays sokoban M Taufeeque, P Quirke, M Li, C Cundy, AD Tucker, A Gleave, ... arXiv preprint arXiv:2407.15421, 2024 | 24* | 2024 |
Antonin RaffinDLRVerified email at dlr.de
