Ruben Glatt

According to our database, Ruben Glatt authored at least 24 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Review on Simulation Platforms for Agent-Based Modeling in Electrified Transportation.

Donté Harris, Felipe Leno da Silva, Wencong Su, Ruben Glatt

IEEE Trans. Intell. Transp. Syst., February, 2024

Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization.

Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt, Shusen Liu

CoRR, 2024

2023

Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging.

Ruben Glatt, Shusen Liu

CoRR, 2023

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition.

Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava

CoRR, 2023

Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters.

Van-Hai Bui, Fangyuan Chang, Wencong Su, Mengqi Wang, Yi Lu Murphey, Felipe Leno da Silva, Can Huang, Lingxiao Xue, Ruben Glatt

Proceedings of the International Conference on Computing, Networking and Communications, 2023

2022

Deep Neural Network-Based Surrogate Model for Optimal Component Sizing of Power Converters Using Deep Reinforcement Learning.

Van-Hai Bui, Fangyuan Chang, Wencong Su, Mengqi Wang, Yi Lu Murphey, Felipe Leno da Silva, Can Huang, Lingxiao Xue, Ruben Glatt

IEEE Access, 2022

A Unified Framework for Deep Symbolic Regression.

Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning.

Edward Rusu, Ruben Glatt

J. Open Source Softw., 2021

Symbolic Regression via Neural-Guided Genetic Programming Population Seeding.

T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

CoRR, 2021

Improving exploration in policy gradient search: Application to symbolic optimization.

Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

CoRR, 2021

Hybrid Information-driven Multi-agent Reinforcement Learning.

William A. Dawson, Ruben Glatt, Edward Rusu, Braden C. Soper, Ryan A. Goldhahn

CoRR, 2021

Collaborative energy demand response with decentralized actor and centralized critic.

Ruben Glatt, Felipe Leno da Silva, Braden Soper, William A. Dawson, Edward Rusu, Ryan A. Goldhahn

Proceedings of the BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Coimbra, Portugal, November 17, 2021

Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding.

T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Discovering symbolic policies with deep reinforcement learning.

Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

<i>DECAF</i>: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning.

Ruben Glatt, Felipe Leno da Silva, Reinaldo Augusto da Costa Bianchi, Anna Helena Reali Costa

Expert Syst. Appl., 2020

2019

MOO-MDP: An Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning.

Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa

IEEE Trans. Cybern., 2019

Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning.

Jacob F. Pettit, Ruben Glatt, Jonathan R. Donadee, Brenden K. Petersen

CoRR, 2019

2018

A Framework to Discover and Reuse Object-Oriented Options in Reinforcement Learning.

Rodrigo Cesar Bonini, Felipe Leno da Silva, Ruben Glatt, Edison Spina, Anna Helena Reali Costa

Proceedings of the 7th Brazilian Conference on Intelligent Systems, 2018

2017

Simultaneously Learning and Advising in Multiagent Reinforcement Learning.

Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

An Advising Framework for Multiagent Reinforcement Learning Systems.

Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Improving Deep Reinforcement Learning with Knowledge Transfer.

Ruben Glatt, Anna Helena Reali Costa

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Policy Reuse in Deep Reinforcement Learning.

Ruben Glatt, Anna Helena Reali Costa

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Object-Oriented Reinforcement Learning in Cooperative Multiagent Domains.

Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa

Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2016

Towards Knowledge Transfer in Deep Reinforcement Learning.

Ruben Glatt, Felipe Leno da Silva, Anna Helena Reali Costa

Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2016