The option keyboard: Combining skills in reinforcement learning A Barreto, D Borsa, S Hou, G Comanici, E Aygün, P Hamel, D Toyama, ... Advances in Neural Information Processing Systems 32, 2019 | 104 | 2019 |
What can i do here? a theory of affordances in reinforcement learning K Khetarpal, Z Ahmed, G Comanici, D Abel, D Precup International Conference on Machine Learning, 5243-5253, 2020 | 73 | 2020 |
Androidenv: A reinforcement learning platform for android D Toyama, P Hamel, A Gergely, G Comanici, A Glaese, Z Ahmed, ... arXiv preprint arXiv:2105.13231, 2021 | 55 | 2021 |
Optimal policy switching algorithms for reinforcement learning G Comanici, D Precup Proceedings of the 9th International Conference on Autonomous Agents and …, 2010 | 48 | 2010 |
On-the-fly algorithms for bisimulation metrics G Comanici, P Panangaden, D Precup 2012 ninth international conference on quantitative evaluation of systems …, 2012 | 24 | 2012 |
Basis function discovery using spectral clustering and bisimulation metrics G Comanici, D Precup International Workshop on Adaptive and Learning Agents, 85-99, 2011 | 21 | 2011 |
Representation discovery for mdps using bisimulation metrics S Ruan, G Comanici, P Panangaden, D Precup Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 17 | 2015 |
Temporally abstract partial models K Khetarpal, Z Ahmed, G Comanici, D Precup Advances in Neural Information Processing Systems 34, 1979-1991, 2021 | 12 | 2021 |
An empirical analysis of off-policy learning in discrete mdps C Păduraru, D Precup, J Pineau, G Comănici European Workshop on Reinforcement Learning, 89-102, 2013 | 12 | 2013 |
Knowledge representation for reinforcement learning using general value functions G Comanici, D Precup, A Barreto, DK Toyama, E Aygün, P Hamel, ... | 10 | 2018 |
Basis refinement strategies for linear value function approximation in MDPs G Comanici, D Precup, P Panangaden Advances in neural information processing systems 28, 2015 | 9 | 2015 |
Vision-Language Models as a Source of Rewards K Baumli, S Baveja, F Behbahani, H Chan, G Comanici, S Flennerhag, ... arXiv preprint arXiv:2312.09187, 2023 | 8 | 2023 |
What can I do here K Khetarpal, Z Ahmed, G Comanici, D Abel, D Precup A theory of affordances in reinforcement learning. arXiv [cs. LG], 2020 | 6 | 2020 |
AndroidEnv: A Reinforcement Learning Platform for Android. abs/2105.13231 (2021) D Toyama, P Hamel, A Gergely, G Comanici, A Glaese, Z Ahmed, ... arXiv preprint cs.LG/2105.13231, 2021 | 5 | 2021 |
Finding increasingly large extremal graphs with alphazero and tabu search A Mehrabian, A Anand, H Kim, N Sonnerat, M Balog, G Comanici, ... arXiv preprint arXiv:2311.03583, 2023 | 4 | 2023 |
A study of off-policy learning in computational sustainability C Paduraru, D Precup, J Pineau, G Comanici European Workshop on Reinforcement Learning (EWRL) 24, 89-102, 2012 | 4 | 2012 |
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning G Comanici, A Glaese, A Gergely, D Toyama, Z Ahmed, T Jackson, ... arXiv preprint arXiv:2204.10374, 2022 | 1 | 2022 |
Controlling computing devices using hierarchical agents GT Comanici, AMC Glaese, A Gergely, Z Ahmed, T Jackson, D Precup US Patent App. 18/105,180, 2024 | | 2024 |
Representation discovery for Markov decision processes using behavioural similarity G Comanici McGill University (Canada), 2016 | | 2016 |
Optimal Time Scales for Reinforcement Learning Behaviour Strategies G Comanici, D Precup McGill University, 2010 | | 2010 |