Towards Knowledge Transfer in Deep Reinforcement Learning. - Ruben Glatt; Felipe Leno da Silva; Anna Helena Reali Costa (BRACIS, 2016)

Object-Oriented Reinforcement Learning in Cooperative Multiagent Domains. - Felipe Leno da Silva; Ruben Glatt; Anna Helena Reali Costa (BRACIS, 2016)

Policy Reuse in Deep Reinforcement Learning. - Ruben Glatt; Anna Helena Reali Costa (AAAI, 2017)

Improving Deep Reinforcement Learning with Knowledge Transfer. - Ruben Glatt; Anna Helena Reali Costa (AAAI, 2017)

An Advising Framework for Multiagent Reinforcement Learning Systems. - Felipe Leno da Silva; Ruben Glatt; Anna Helena Reali Costa (AAAI, 2017)

Simultaneously Learning and Advising in Multiagent Reinforcement Learning. - Felipe Leno da Silva; Ruben Glatt; Anna Helena Reali Costa (AAMAS, 2017)

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 (BRACIS, 2018)

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)

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)

DECAF: 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)

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 (ICML, 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 (NeurIPS, 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 (BuildSys, 2021)

Hybrid Information-driven Multi-agent Reinforcement Learning. - William A. Dawson; Ruben Glatt; Edward Rusu; Braden C. Soper; Ryan A. Goldhahn (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)

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)

Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning. - Edward Rusu; Ruben Glatt (J. Open Source Softw., 2021)

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 (NeurIPS, 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)

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 (ICNC, 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; et al. (CoRR, 2023)

Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging. - Ruben Glatt; Shusen Liu (CoRR, 2023)

Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization. - Hongjun Choi; Jayaraman J. Thiagarajan; Ruben Glatt; Shusen Liu 001 (CoRR, 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., 2024)

Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics. - Conor F. Hayes; Felipe Leno da Silva; Jiachen Yang; T. Nathan Mundhenk; Chak Shing Lee; Jacob F. Pettit; Claudio Santiago; Sookyung Kim; Joanne Taery Kim; Ignacio Aravena Solis; Ruben Glatt; Andre R. Goncalves; et al. (CoRR, 2025)

VERIRAG: Healthcare Claim Verification via Statistical Audit in Retrieval-Augmented Generation. - Shubham Mohole; Hongjun Choi; Shusen Liu 001; Christine Klymko; Shashank Kushwaha; Derek Shi; Wesam Sakla; Sainyam Galhotra; Ruben Glatt (CoRR, 2025)

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. - 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; et al. (IEEE Trans. Evol. Comput., 2025)