| The option-critic architecture PL Bacon, J Harb, D Precup Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 1278 | 2017 |
| Conditional computation in neural networks for faster models E Bengio, PL Bacon, J Pineau, D Precup arXiv preprint arXiv:1511.06297, 2015 | 349 | 2015 |
| When waiting is not an option: Learning options with a deliberation cost J Harb, PL Bacon, M Klissarov, D Precup Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 166 | 2018 |
| The primacy bias in deep reinforcement learning E Nikishin, M Schwarzer, P D’Oro, PL Bacon, A Courville International conference on machine learning, 16828-16847, 2022 | 143 | 2022 |
| Sample-efficient reinforcement learning by breaking the replay ratio barrier P D'Oro, M Schwarzer, E Nikishin, PL Bacon, MG Bellemare, A Courville Deep Reinforcement Learning Workshop NeurIPS 2022, 2022 | 73 | 2022 |
| Learnings options end-to-end for continuous action tasks M Klissarov, PL Bacon, J Harb, D Precup arXiv preprint arXiv:1712.00004, 2017 | 64 | 2017 |
| Options of interest: Temporal abstraction with interest functions K Khetarpal, M Klissarov, M Chevalier-Boisvert, PL Bacon, D Precup Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4444-4451, 2020 | 50 | 2020 |
| Convergent tree backup and retrace with function approximation A Touati, PL Bacon, D Precup, P Vincent International Conference on Machine Learning, 4955-4964, 2018 | 50 | 2018 |
| Learning robust options D Mankowitz, T Mann, PL Bacon, D Precup, S Mannor Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 48 | 2018 |
| Understanding the curse of horizon in off-policy evaluation via conditional importance sampling Y Liu, PL Bacon, E Brunskill International Conference on Machine Learning, 6184-6193, 2020 | 43 | 2020 |
| Policy evaluation networks J Harb, T Schaul, D Precup, PL Bacon arXiv preprint arXiv:2002.11833, 2020 | 41 | 2020 |
| Direct behavior specification via constrained reinforcement learning J Roy, R Girgis, J Romoff, PL Bacon, C Pal arXiv preprint arXiv:2112.12228, 2021 | 37 | 2021 |
| Control-oriented model-based reinforcement learning with implicit differentiation E Nikishin, R Abachi, R Agarwal, PL Bacon Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7886-7894, 2022 | 34 | 2022 |
| Motif: Intrinsic motivation from artificial intelligence feedback M Klissarov, P D'Oro, S Sodhani, R Raileanu, PL Bacon, P Vincent, ... arXiv preprint arXiv:2310.00166, 2023 | 30 | 2023 |
| Temporal Representation Learning PL Bacon McGill University (Canada), 2018 | 30 | 2018 |
| Learning with options that terminate off-policy A Harutyunyan, P Vrancx, PL Bacon, D Precup, A Nowe Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 27 | 2018 |
| An information-theoretic perspective on credit assignment in reinforcement learning D Arumugam, P Henderson, PL Bacon arXiv preprint arXiv:2103.06224, 2021 | 22 | 2021 |
| When do transformers shine in rl? decoupling memory from credit assignment T Ni, M Ma, B Eysenbach, PL Bacon Advances in Neural Information Processing Systems 36, 2024 | 21 | 2024 |
| Continuous-time meta-learning with forward mode differentiation T Deleu, D Kanaa, L Feng, G Kerg, Y Bengio, G Lajoie, PL Bacon arXiv preprint arXiv:2203.01443, 2022 | 21 | 2022 |
| Neural algorithmic reasoners are implicit planners AI Deac, P Veličković, O Milinkovic, PL Bacon, J Tang, M Nikolic Advances in Neural Information Processing Systems 34, 15529-15542, 2021 | 20 | 2021 |
Doina PrecupDeepMind and McGill UniversityVerified email at cs.mcgill.ca
