Rodrigo Toro Icarte

发表

LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning

Alberto Camacho, Sheila A. McIlraith, Richard Anthony Valenzano, 2019, IJCAI.

Advice-Based Exploration in Model-Based Reinforcement Learning

Sheila A. McIlraith, Richard Anthony Valenzano, Toryn Q. Klassen, 2018, Canadian Conference on AI.

The act of remembering: a study in partially observable reinforcement learning pdf

Sheila A. McIlraith, Amir-massoud Farahmand, Toryn Q. Klassen, 2020, ArXiv.

Interpretable Sequence Classification via Discrete Optimization pdf

Sheila A. McIlraith, Maayan Shvo, Rodrigo Toro Icarte, 2020, AAAI.

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning pdf

Sheila A. McIlraith, Toryn Q. Klassen, Richard Valenzano, 2020, ArXiv.

Training Binarized Neural Networks Using MIP and CP

Sheila A. McIlraith, J. Christopher Beck, André Augusto Ciré, 2019, CP.

Learning Reward Machines for Partially Observable Reinforcement Learning

Sheila A. McIlraith, Toryn Q. Klassen, Rodrigo Toro Icarte, 2019, NeurIPS.

Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning

Sheila A. McIlraith, Richard Anthony Valenzano, Toryn Q. Klassen, 2018, ICML.

Symbolic Plans as High-Level Instructions for Reinforcement Learning

Sheila A. McIlraith, Rodrigo Toro Icarte, Xi Yan, 2020, ICAPS.

Teaching Multiple Tasks to an RL Agent using LTL

Sheila A. McIlraith, Richard Anthony Valenzano, Toryn Q. Klassen, 2018, AAMAS.

How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval

Jorge A. Baier, Alvaro Soto, Cristian Ruz, 2017, IJCAI.

LTL2Action: Generalizing LTL Instructions for Multi-Task RL pdf

Sheila A. McIlraith, Pashootan Vaezipoor, Andrew Li, 2021, ICML.

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning pdf

Sheila A. McIlraith, Toryn Q. Klassen, Richard Valenzano, 2020, J. Artif. Intell. Res..

Learning Reward Machines: A Study in Partially Observable Reinforcement Learning pdf

Sheila A. McIlraith, Toryn Q. Klassen, Richard Valenzano, 2021, ArXiv.

Be Considerate: Objectives, Side Effects, and Deciding How to Act pdf

Sheila A. McIlraith, Toryn Q. Klassen, Rodrigo Toro Icarte, 2021, ArXiv.

AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning pdf

Sheila A. McIlraith, Zhiming Hu, Iqbal Mohomed, 2021, Canadian Conference on AI.

Searching for Markovian Subproblems to Address Partially Observable Reinforcement Learning

Toryn Q. Klassen, Rodrigo Toro Icarte, 2019 .

Leveraging Symbolic Planning Models in Hierarchical Reinforcement Learning

Sheila A. McIlraith, Rodrigo Toro Icarte, León Illanes, 2019 .

Using Advice in Model-Based Reinforcement Learning

Sheila A. McIlraith, Toryn Q. Klassen, Richard Valenzano, 2017 .

Symbolic Planning and Model-Free Reinforcement Learning: Training Taskable Agents

Xi Yan, Rodrigo Toro Icarte, León Illanes, 2019 .

COMMONSENSE ONTOLOGY IMPROVE PERFORMANCE OF LEARNING-BASED IMAGE RETRIEVAL ?

Rodrigo Toro Icarte, 2015 .

Be Considerate: Avoiding Negative Side Effects in Reinforcement Learning

Sheila A. McIlraith, Toryn Q. Klassen, Rodrigo Toro Icarte, 2022, AAMAS.

Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks

Sheila A. McIlraith, Rodrigo Toro Icarte, Andrew C. Li, 2022, ArXiv.

Exploring Long-Horizon Reasoning with Deep RL in Combinatorially Hard Tasks

Sheila A. McIlraith, Rodrigo Toro Icarte, Andrew C. Li, 2022 .

Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines

Sheila A. McIlraith, Toryn Q. Klassen, Rodrigo Toro Icarte, 2022, ArXiv.

Learning reward machines: A study in partially observable reinforcement learning

Sheila A. McIlraith, Toryn Q. Klassen, Rodrigo Toro Icarte, 2023, Artif. Intell..

Learning Belief Representations for Partially Observable Deep RL

Sheila A. McIlraith, Toryn Q. Klassen, Rodrigo Toro Icarte, 2023, ICML.