| Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith ICML 2018, 2112--2121, 2018 | 305* | 2018 |
| LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning A Camacho, R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith IJCAI 2019, 6065-6073, 2019 | 235 | 2019 |
| Reward machines: Exploiting reward function structure in reinforcement learning R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith Journal of Artificial Intelligence Research 73, 173-208, 2022 | 179 | 2022 |
| Teaching Multiple Tasks to an RL Agent using LTL R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith AAMAS 2018, 452-461, 2018 | 142 | 2018 |
| Learning Reward Machines for Partially Observable Reinforcement Learning R Toro Icarte, E Waldie, TQ Klassen, R Valenzano, M Castro, SA McIlraith NeurIPS 2019, 15497--15508, 2019 | 130 | 2019 |
| 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 |
| Towards the Role of Theory of Mind in Explanation M Shvo, TQ Klassen, SA McIlraith EXTRAAMAS 2020: Explainable, Transparent Autonomous Agents and Multi-Agent …, 2020 | 28 | 2020 |
| Epistemic Plan Recognition M Shvo, TQ Klassen, S Sohrabi, SA McIlraith AAMAS 2020, 1251-1259, 2020 | 25 | 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 | 16 | 2022 |
| Resolving Misconceptions about the Plans of Agents via Theory of Mind M Shvo, TQ Klassen, SA McIlraith Proceedings of the International Conference on Automated Planning and …, 2022 | 13 | 2022 |
| Learning to follow instructions in text-based games M Tuli, AC Li, P Vaezipoor, TQ Klassen, S Sanner, SA McIlraith Advances in Neural Information Processing Systems 35, 19441-19455, 2022 | 11 | 2022 |
| Independence of tabulation-based hash classes TQ Klassen, P Woelfel Latin American Symposium on Theoretical Informatics, 506-517, 2012 | 11 | 2012 |
| Planning to Avoid Side Effects TQ Klassen, SA McIlraith, C Muise, J Xu Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence …, 2022 | 10 | 2022 |
| Learning Reward Machines: A Study in Partially Observable Reinforcement Learning RT Icarte, TQ Klassen, R Valenzano, MP Castro, E Waldie, SA McIlraith Artificial Intelligence, 103989, 2023 | 9 | 2023 |
| 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 |
| Towards tractable inference for resource-bounded agents TQ Klassen, SA McIlraith, HJ Levesque Commonsense 2015, 89--95, 2015 | 8 | 2015 |
| 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 | 7 | 2022 |
| Using Advice in Model-Based Reinforcement Learning R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith RLDM 2017, 2017 | 7* | 2017 |
| Searching for Markovian Subproblems to Address Partially Observable Reinforcement Learning R Toro Icarte, E Waldie, TQ Klassen, R Valenzano, MP Castro, ... RLDM 2019, 2019 | 6* | 2019 |
| Specifying Plausibility Levels for Iterated Belief Change in the Situation Calculus TQ Klassen, SA McIlraith, HJ Levesque KR 2018, 257-266, 2018 | 6 | 2018 |
Sheila McIlraithProfessor of Computer Science, University of Toronto在 cs.toronto.edu 的电子邮件经过验证
