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2020 – today
- 2024
[j5]Donté Harris
, Felipe Leno da Silva, Wencong Su, Ruben Glatt:
A Review on Simulation Platforms for Agent-Based Modeling in Electrified Transportation. IEEE Trans. Intell. Transp. Syst. 25(2): 1131-1147 (2024)- [i7]Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt, Shusen Liu:
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization. CoRR abs/2407.00356 (2024) - 2023
- [c12]Van-Hai Bui, Fangyuan Chang, Wencong Su, Mengqi Wang, Yi Lu Murphey, Felipe Leno da Silva, Can Huang, Lingxiao Xue, Ruben Glatt:
Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters. ICNC 2023: 25-29 - [i6]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:
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023) - [i5]Ruben Glatt, Shusen Liu:
Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging. CoRR abs/2306.17400 (2023) - 2022
- [j4]Van-Hai Bui, Fangyuan Chang, Wencong Su, Mengqi Wang, Yi Lu Murphey, Felipe Leno da Silva, Can Huang, Lingxiao Xue, Ruben Glatt:
Deep Neural Network-Based Surrogate Model for Optimal Component Sizing of Power Converters Using Deep Reinforcement Learning. IEEE Access 10: 78702-78712 (2022) - [c11]Mikel Landajuela, Chak Shing Lee, Jiachen Yang, Ruben Glatt, Cláudio P. Santiago, Ignacio Aravena, Terrell Nathan Mundhenk, Garrett Mulcahy, Brenden K. Petersen:
A Unified Framework for Deep Symbolic Regression. NeurIPS 2022 - 2021
- [j3]Edward Rusu, Ruben Glatt:
Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning. J. Open Source Softw. 6(64): 3424 (2021) - [c10]Mikel Landajuela, Brenden K. Petersen, Sookyung Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Discovering symbolic policies with deep reinforcement learning. ICML 2021: 5979-5989 - [c9]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. NeurIPS 2021: 24912-24923 - [c8]Ruben Glatt, Felipe Leno da Silva, Braden Soper, William A. Dawson, Edward Rusu, Ryan A. Goldhahn:
Collaborative energy demand response with decentralized actor and centralized critic. BuildSys@SenSys 2021: 333-337 - [i4]William A. Dawson, Ruben Glatt, Edward Rusu, Braden C. Soper, Ryan A. Goldhahn:
Hybrid Information-driven Multi-agent Reinforcement Learning. CoRR abs/2102.01004 (2021) - [i3]Mikel Landajuela, Brenden K. Petersen, Soo K. Kim, Cláudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol:
Improving exploration in policy gradient search: Application to symbolic optimization. CoRR abs/2107.09158 (2021) - [i2]T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Cláudio P. Santiago, Daniel M. Faissol, Brenden K. Petersen:
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. CoRR abs/2111.00053 (2021) - 2020
- [j2]Ruben Glatt, Felipe Leno da Silva, Reinaldo Augusto da Costa Bianchi, Anna Helena Reali Costa:
DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning. Expert Syst. Appl. 156: 113420 (2020)
2010 – 2019
- 2019
- [j1]Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa:
MOO-MDP: An Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning. IEEE Trans. Cybern. 49(2): 567-579 (2019) - [i1]Jacob F. Pettit, Ruben Glatt, Jonathan R. Donadee, Brenden K. Petersen:
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning. CoRR abs/1912.03408 (2019) - 2018
- [c7]Rodrigo Cesar Bonini, Felipe Leno da Silva, Ruben Glatt, Edison Spina, Anna Helena Reali Costa:
A Framework to Discover and Reuse Object-Oriented Options in Reinforcement Learning. BRACIS 2018: 109-114 - 2017
- [c6]Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa:
An Advising Framework for Multiagent Reinforcement Learning Systems. AAAI 2017: 4913-4914 - [c5]Ruben Glatt, Anna Helena Reali Costa:
Policy Reuse in Deep Reinforcement Learning. AAAI 2017: 4929-4930 - [c4]Ruben Glatt, Anna Helena Reali Costa:
Improving Deep Reinforcement Learning with Knowledge Transfer. AAAI 2017: 5036-5037 - [c3]Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa:
Simultaneously Learning and Advising in Multiagent Reinforcement Learning. AAMAS 2017: 1100-1108 - 2016
- [c2]Felipe Leno da Silva, Ruben Glatt, Anna Helena Reali Costa:
Object-Oriented Reinforcement Learning in Cooperative Multiagent Domains. BRACIS 2016: 19-24 - [c1]Ruben Glatt, Felipe Leno da Silva, Anna Helena Reali Costa:
Towards Knowledge Transfer in Deep Reinforcement Learning. BRACIS 2016: 91-96
Coauthor Index
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