| Using reward machines for high-level task specification and decomposition in reinforcement learning R Toro Icarte, T Klassen, R Valenzano, S McIlraith International Conference on Machine Learning, 2112-2121, 2018 | 325* | 2018 |
| LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning A Camacho, R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith Proceedings of the 28th International Joint Conference on Artificial …, 2019 | 251 | 2019 |
| Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith arXiv preprint arXiv:2010.03950, 2020 | 202* | 2020 |
| Learning Reward Machines for Partially Observable Reinforcement Learning R Toro Icarte, E Waldie, T Klassen, R Valenzano, M Castro, S McIlraith Advances in Neural Information Processing Systems, 15497-15508, 2019 | 149 | 2019 |
| Teaching multiple tasks to an RL agent using LTL R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith Proceedings of the 17th International Conference on Autonomous Agents and …, 2018 | 146 | 2018 |
| Symbolic Plans as High-Level Instructions for Reinforcement Learning L Illanes, X Yan, R Toro Icarte, SA McIlraith Proceedings of the International Conference on Automated Planning and …, 2020 | 118 | 2020 |
| LTL2Action: Generalizing LTL Instructions for Multi-Task RL P Vaezipoor, A Li, R Toro Icarte, S McIlraith arXiv preprint arXiv:2102.06858, 2021 | 74 | 2021 |
| Advice-based exploration in model-based reinforcement learning R Toro Icarte, TQ Klassen, RA Valenzano, SA McIlraith Canadian Conference on Artificial Intelligence, 72-83, 2018 | 30 | 2018 |
| Training Binarized Neural Networks using MIP and CP R Toro Icarte, L Illanes, MP Castro, AA Cire, SA McIlraith, JC Beck Proceedings of the 25th International Conference on Principles and Practice …, 2019 | 29 | 2019 |
| How a general-purpose commonsense ontology can improve performance of learning-based image retrieval R Toro Icarte, JA Baier, C Ruz, A Soto arXiv preprint arXiv:1705.08844, 2017 | 29 | 2017 |
| Interpretable Sequence Classification via Discrete Optimization M Shvo, AC Li, R Toro Icarte, SA McIlraith arXiv preprint arXiv:2010.02819, 2020 | 19 | 2020 |
| Be considerate: Avoiding negative side effects in reinforcement learning P Alizadeh Alamdari, TQ Klassen, R Toro Icarte, SA McIlraith Proceedings of the 21st International Conference on Autonomous Agents and …, 2022 | 17 | 2022 |
| Symbolic Planning and Model-Free Reinforcement Learning: Training Taskable Agents L Illanes, X Yan, R Toro Icarte, SA McIlraith Proceedings of the 4th Multi-disciplinary Conference on Reinforcement …, 2019 | 14 | 2019 |
| Solving task scheduling problems in dew computing via deep reinforcement learning P Sanabria, TF Tapia, R Toro Icarte, A Neyem Applied Sciences 12 (14), 7137, 2022 | 10 | 2022 |
| Noisy symbolic abstractions for deep RL: A case study with reward machines AC Li, Z Chen, P Vaezipoor, TQ Klassen, RT Icarte, SA McIlraith arXiv preprint arXiv:2211.10902, 2022 | 9 | 2022 |
| The act of remembering: a study in partially observable reinforcement learning R Toro Icarte, R Valenzano, TQ Klassen, P Christoffersen, A Farahmand, ... arXiv preprint arXiv:2010.01753, 2020 | 8 | 2020 |
| AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning. M Shvo, Z Hu, RT Icarte, I Mohomed, AD Jepson, SA McIlraith Canadian AI, 2021 | 7 | 2021 |
| Using Advice in Model-Based Reinforcement Learning R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision …, 2017 | 7* | 2017 |
| Reward Machines RAT Icarte University of Toronto (Canada), 2022 | 6 | 2022 |
| Searching for Markovian Subproblems to Address Partially Observable Reinforcement Learning R Toro Icarte, E Waldie, TQ Klassen, R Valenzano, MP Castro, ... Proceedings of the 4th Multi-disciplinary Conference on Reinforcement …, 2019 | 6* | 2019 |
Sheila McIlraithProfessor of Computer Science, University of Toronto在 cs.toronto.edu 的电子邮件经过验证
