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Rodrigo Andrés Toro Icarte

Assistant Professor

Pontificia Universidad Católica de Chile

Santiago, Chile

Artificial Intelligence, Machine Learning, Reinforcement Learning, Deep Learning, Discrete Optimization, Knowledge Representation, and Commonsense Reasoning.

Computer Science, UNIVERSITY OF TORONTO. Canada, 2018
Artificial Intelligence, UNIVERSITY OF TORONTO. Canada, 2022
Computer Science, PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE. Chile, 2015

Assistant professor Full Time

PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE

Engineering

Chile

2021 - A la fecha


Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning Toro Icarte, Rodrigo; Klassen, Toryn; Valenzano, Richard; McIlraith, Sheila Article JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2022)
Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning Sanabria, Pablo; Tapia, Tomas Felipe; Icarte, Rodrigo Toro; NEYEM, ANDRES Article ISI APPLIED SCIENCES-BASEL (2022)
Training Binarized Neural Networks Using MIP and CP Icarte, Rodrigo Toro; Illanes, Leon; Castro, Margarita P.; Cire, Andre A.; McIlraith, Sheila A.; Beck, J. Christopher; Schiex, T; DeGivry, S Article ISI BIO-INSPIRED SYSTEMS AND APPLICATIONS: FROM ROBOTICS TO AMBIENT INTELLIGENCE, PT II (2019)


Be Considerate: Avoiding Negative Side Effects in Reinforcement Learning Alizadeh Alamdari, Parand; Klassen, Toryn; Toro Icarte, Rodrigo; McIlraith, Sheila ConferencePaper (2022)
Real-Time Heuristic Search with LTLf Goals Middleton, Jaime; Toro Icarte, Rodrigo; Baier, Jorge ConferencePaper (2022)
Interpretable Sequence Classification via Discrete Optimization Shvo, Maayan; Li, Andrew; Toro Icarte, Rodrigo; McIlraith, Sheila ConferencePaper (2021)
LTL2Action: Generalizing LTL Instructions for Multi-Task RL Vaezipoor, Pashootan; Li, Andrew; Toro Icarte, Rodrigo; McIlraith, Sheila ConferencePaper (2021)
Symbolic Plans as High-Level Instructions for Reinforcement Learning Illanes, Leon; Yan, Xi; Toro Icarte, Rodrigo; McIlraith, Sheila ConferencePaper (2020)
Learning Reward Machines for Partially Observable Reinforcement Learning Toro Icarte, Rodrigo; Waldie, Ethan; Klassen, Toryn; Valenzano, Richard; Castro, Margarita; McIlraith, Sheila ConferencePaper (2019)
LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning Camacho, Alberto; Toro Icarte, Rodrigo; Klassen, Toryn; Valenzano, Richard; McIlraith, Sheila ConferencePaper (2019)
Advice-Based Exploration in Model-Based Reinforcement Learning Toro Icarte, Rodrigo; Klassen, Toryn; Valenzano, Richard; McIlraith, Sheila ConferencePaper (2018)
Teaching Multiple Tasks to an RL Agent using LTL Toro Icarte, Rodrigo; Klassen, Toryn; Valenzano, Richard; McIlraith, Sheila ConferencePaper (2018)
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning Toro Icarte, Rodrigo; Klassen, Toryn; Valenzano, Richard; McIlraith, Sheila ConferencePaper (2018)
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval Toro Icarte, Rodrigo; Baier, Jorge A.; Ruz, Cristián; Soto, Alvaro ConferencePaper (2017)

Rodrigo Toro

Assistant Professor

Computer Science

Pontificia Universidad Católica de Chile

Santiago, Chile

Margarita Castro

Assistant Professor

Ingeniería Industrial y de Sistemas

Pontificia Universidad Católica de Chile

Santiago, Chile

Rodrigo Andrés Toro Icarte

Líneas de Investigación

Educación

Computer Science, UNIVERSITY OF TORONTO. Canada, 2018

Artificial Intelligence, UNIVERSITY OF TORONTO. Canada, 2022

Computer Science, PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE. Chile, 2015

Experiencia Académica

Assistant professor Full Time

Article (3)

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning

Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning

Training Binarized Neural Networks Using MIP and CP

ConferencePaper (11)

Be Considerate: Avoiding Negative Side Effects in Reinforcement Learning

Real-Time Heuristic Search with LTLf Goals

Interpretable Sequence Classification via Discrete Optimization

LTL2Action: Generalizing LTL Instructions for Multi-Task RL

Symbolic Plans as High-Level Instructions for Reinforcement Learning

Learning Reward Machines for Partially Observable Reinforcement Learning

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

Advice-Based Exploration in Model-Based Reinforcement Learning

Teaching Multiple Tasks to an RL Agent using LTL

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

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

Rodrigo Toro

Margarita Castro