Rodrigo Toro Icarte

Orcid: 0000-0002-7734-099X

According to our database1, Rodrigo Toro Icarte authored at least 24 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
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Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2024
Reward Machines for Deep RL in Noisy and Uncertain Environments.
CoRR, 2024

On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Learning reward machines: A study in partially observable reinforcement learning.
Artif. Intell., October, 2023

Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior.
CoRR, 2023

Learning Belief Representations for Partially Observable Deep RL.
Proceedings of the International Conference on Machine Learning, 2023

2022
Reward Machines.
PhD thesis, 2022

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning.
J. Artif. Intell. Res., 2022

Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines.
CoRR, 2022

Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks.
CoRR, 2022

Real-Time Heuristic Search with LTLf Goals.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Be Considerate: Avoiding Negative Side Effects in Reinforcement Learning.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021
Be Considerate: Objectives, Side Effects, and Deciding How to Act.
CoRR, 2021

LTL2Action: Generalizing LTL Instructions for Multi-Task RL.
Proceedings of the 38th International Conference on Machine Learning, 2021

AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning.
Proceedings of the 34th Canadian Conference on Artificial Intelligence, 2021

Interpretable Sequence Classification via Discrete Optimization.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
The act of remembering: a study in partially observable reinforcement learning.
CoRR, 2020

Symbolic Plans as High-Level Instructions for Reinforcement Learning.
Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling, 2020

2019
Learning Reward Machines for Partially Observable Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Training Binarized Neural Networks Using MIP and CP.
Proceedings of the Principles and Practice of Constraint Programming, 2019

2018
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Teaching Multiple Tasks to an RL Agent using LTL.
Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

Advice-Based Exploration in Model-Based Reinforcement Learning.
Proceedings of the Advances in Artificial Intelligence, 2018

2017
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017


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