Rodrigo Toro Icarte
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引用次数
引用次数
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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
64*2018
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
562018
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
442019
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
202019
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
162020
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
122017
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
112018
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
102019
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
62019
Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
R Toro Icarte, TQ Klassen, R Valenzano, SA McIlraith
arXiv preprint arXiv:2010.03950, 2020
42020
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
4*2019
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
32017
Interpretable Sequence Classification via Discrete Optimization
M Shvo, AC Li, R Toro Icarte, SA McIlraith
arXiv preprint arXiv:2010.02819, 2020
12020
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
12020
Be Considerate: Objectives, Side Effects, and Deciding How to Act
PA Alamdari, TQ Klassen, RT Icarte, SA McIlraith
arXiv preprint arXiv:2106.02617, 2021
2021
Be Considerate: Objectives, Side Effects, and Deciding How to Act
P Alizadeh Alamdari, TQ Klassen, R Toro Icarte, SA McIlraith
arXiv e-prints, arXiv: 2106.02617, 2021
2021
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning
M Shvo, Z Hu, RT Icarte, I Mohomed, A Jepson, SA McIlraith
arXiv preprint arXiv:2106.00133, 2021
2021
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
P Vaezipoor, A Li, R Toro Icarte, S McIlraith
arXiv preprint arXiv:2102.06858, 2021
2021
Can a general-purpose commonsense ontology improve performance of learning-based image retrieval?
RA Toro Icarte
2015
Learning Reward Machines for Partially Observable Reinforcement Learning (Abridged Report)
R Toro Icarte, E Waldie, TQ Klassen, R Valenzano, AI Element, MP Castro, ...
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