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Ruben Glatt
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2020 – today
- 2024
- [j5]Donté Harris
, Felipe Leno da Silva
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
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
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
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
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 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
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
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
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
Object-Oriented Reinforcement Learning in Cooperative Multiagent Domains. BRACIS 2016: 19-24 - [c1]Ruben Glatt
Towards Knowledge Transfer in Deep Reinforcement Learning. BRACIS 2016: 91-96
Coauthor Index
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last updated on 2024-12-05 20:47 CET by the dblp team
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