| 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 | 2899 | 2021 |
| Stable baselines A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ... | 980 | 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 | 458 | 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 | 300 | 2016 |
| imitation: Clean imitation learning implementations A Gleave, M Taufeeque, J Rocamonde, E Jenner, SH Wang, S Toyer, ... arXiv preprint arXiv:2211.11972, 2022 | 92* | 2022 |
| Quantifying differences in reward functions A Gleave, M Dennis, S Legg, S Russell, J Leike International Conference on Learning Representations, 2021 | 75 | 2021 |
| Inverse reinforcement learning for video games A Tucker, A Gleave, S Russell Deep Reinforcement Learning Workshop at NeurIPS, 2018 | 66 | 2018 |
| 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 | 63* | 2022 |
| 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 | 52 | 2023 |
| Multi-task maximum entropy inverse reinforcement learning A Gleave, O Habryka GoalsRL Workshop at ICML, 2018 | 48 | 2018 |
| Understanding learned reward functions EJ Michaud, A Gleave, S Russell Deep Reinforcement Learning Workshop at NeurIPS, 2020 | 40 | 2020 |
| Active inverse reward design S Mindermann, R Shah, A Gleave, D Hadfield-Menell GoalsRL Workshop at ICML, 2018 | 35 | 2018 |
| Uncertainty estimation for language reward models A Gleave, G Irving arXiv preprint arXiv:2203.07472, 2022 | 28 | 2022 |
| A primer on maximum causal entropy inverse reinforcement learning A Gleave, S Toyer arXiv preprint arXiv:2203.11409, 2022 | 24 | 2022 |
| Exploiting novel gpt-4 apis K Pelrine, M Taufeeque, M Zając, E McLean, A Gleave arXiv preprint arXiv:2312.14302, 2023 | 21 | 2023 |
| On the fragility of learned reward functions L McKinney, Y Duan, D Krueger, A Gleave arXiv preprint arXiv:2301.03652, 2023 | 21 | 2023 |
| Making compression algorithms for Unicode text A Gleave, C Steinruecken Data Compression Conference, 2017 | 15 | 2017 |
| Preprocessing reward functions for interpretability E Jenner, A Gleave arXiv preprint arXiv:2203.13553, 2022 | 13 | 2022 |
| Scaling laws for data poisoning in llms D Bowen, B Murphy, W Cai, D Khachaturov, A Gleave, K Pelrine arXiv e-prints, arXiv: 2408.02946, 2024 | 11 | 2024 |
| DERAIL: Diagnostic Environments for Reward And Imitation Learning P Freire, A Gleave, S Toyer, S Russell Deep Reinforcement Learning Workshop at NeurIPS, 2020 | 10 | 2020 |
Antonin RaffinDLRZweryfikowany adres z dlr.de