| The option-critic architecture PL Bacon, J Harb, D Precup Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 1103 | 2017 |
| Conditional computation in neural networks for faster models E Bengio, PL Bacon, J Pineau, D Precup arXiv preprint arXiv:1511.06297, 2015 | 300 | 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 | 137 | 2018 |
| Optiongan: Learning joint reward-policy options using generative adversarial inverse reinforcement learning P Henderson, WD Chang, PL Bacon, D Meger, J Pineau, D Precup Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 81 | 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 | 52 | 2022 |
| Learnings options end-to-end for continuous action tasks M Klissarov, PL Bacon, J Harb, D Precup arXiv preprint arXiv:1712.00004, 2017 | 48 | 2017 |
| Learning robust options D Mankowitz, T Mann, PL Bacon, D Precup, S Mannor Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 45 | 2018 |
| Convergent tree backup and retrace with function approximation A Touati, PL Bacon, D Precup, P Vincent International Conference on Machine Learning, 4955-4964, 2018 | 44 | 2018 |
| 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 | 38 | 2020 |
| 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 | 36 | 2020 |
| Policy evaluation networks J Harb, T Schaul, D Precup, PL Bacon arXiv preprint arXiv:2002.11833, 2020 | 33 | 2020 |
| Temporal Representation Learning PL Bacon McGill University (Canada), 2018 | 26 | 2018 |
| 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 | 25 | 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 | 24 | 2022 |
| 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 | 21 | 2018 |
| Direct behavior specification via constrained reinforcement learning J Roy, R Girgis, J Romoff, PL Bacon, C Pal arXiv preprint arXiv:2112.12228, 2021 | 17 | 2021 |
| An information-theoretic perspective on credit assignment in reinforcement learning D Arumugam, P Henderson, PL Bacon arXiv preprint arXiv:2103.06224, 2021 | 17 | 2021 |
| Xlvin: executed latent value iteration nets A Deac, P Veličković, O Milinković, PL Bacon, J Tang, M Nikolić arXiv preprint arXiv:2010.13146, 2020 | 16 | 2020 |
| 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 | 14 | 2022 |
| The barbados 2018 list of open issues in continual learning T Schaul, H van Hasselt, J Modayil, M White, A White, PL Bacon, J Harb, ... arXiv preprint arXiv:1811.07004, 2018 | 14 | 2018 |
Doina PrecupDeepMind and McGill UniversityZweryfikowany adres z cs.mcgill.ca
