ITSUMO: An Intelligent Transportation System for Urban Mobility. - Bruno Castro da Silva; Ana L. C. Bazzan; Gustavo Kuhn Andriotti; Filipe Lopes; Denise de Oliveira (IICS, 2004)

RL-CD: Dealing with Non-Stationarity in Reinforcement Learning. - Bruno Castro da Silva; Eduardo W. Basso; Ana L. C. Bazzan; Paulo Martins Engel (AAAI, 2006)

Improving reinforcement learning with context detection. - Bruno Castro da Silva; Eduardo W. Basso; Filipo Studzinski Perotto; Ana L. C. Bazzan; Paulo Martins Engel (AAMAS, 2006)

ITSUMO: an Intelligent Transportation System for Urban Mobility. - Bruno Castro da Silva; Robert Junges; Denise de Oliveira; Ana L. C. Bazzan (AAMAS, 2006)

Reinforcement Learning based Control of Traffic Lights in Non-stationary Environments: A Case Study in a Microscopic Simulator. - Denise de Oliveira; Ana L. C. Bazzan; Bruno Castro da Silva; Eduardo W. Basso; Luís Nunes (EUMAS, 2006)

Dealing with non-stationary environments using context detection. - Bruno Castro da Silva; Eduardo W. Basso; Ana L. C. Bazzan; Paulo Martins Engel (ICML, 2006)

Distributed constraint propagation for diagnosis of faults in physical processes. - Ana L. C. Bazzan; Bruno Castro da Silva (AAMAS, 2007)

Learning in groups of traffic signals. - Ana L. C. Bazzan; Denise de Oliveira; Bruno Castro da Silva (Eng. Appl. Artif. Intell., 2010)

TD-DeltaPi: A Model-Free Algorithm for Efficient Exploration. - Bruno Castro da Silva; Andrew G. Barto (AAAI, 2012)

Learning Parameterized Skills. - Bruno Castro da Silva; George Dimitri Konidaris; Andrew G. Barto (ICML, 2012)

Biasing the behavior of organizationally adept agents: (extended abstract). - Daniel D. Corkill; Chongjie Zhang; Bruno Castro da Silva; Yoonheui Kim; Daniel Garant; Victor R. Lesser; Xiaoqin Zhang 001 (AAMAS, 2013)

Active Learning of Parameterized Skills. - Bruno Castro da Silva; George Dimitri Konidaris; Andrew G. Barto (ICML, 2014)

Learning parameterized motor skills on a humanoid robot. - Bruno Castro da Silva; Gianluca Baldassarre; George Dimitri Konidaris; Andrew G. Barto (ICRA, 2014)

Energetic Natural Gradient Descent. - Philip S. Thomas; Bruno Castro da Silva; Christoph Dann; Emma Brunskill (ICML, 2016)

Using Topological Statistics to Bias and Accelerate Route Choice: Preliminary Findings in Synthetic and Real-World Road Networks. - Fernando Stefanello; Bruno Castro da Silva; Ana L. C. Bazzan (ATT@IJCAI, 2016)

Context-Based Concurrent Experience Sharing in Multiagent Systems. - Daniel Garant; Bruno Castro da Silva; Victor R. Lesser; Chongjie Zhang (AAMAS, 2017)

A Flexible Approach for Designing Optimal Reward Functions. - Ricardo Grunitzki; Bruno Castro da Silva; Ana L. C. Bazzan (AAMAS, 2017)

Learning to Minimise Regret in Route Choice. - Gabriel de Oliveira Ramos; Bruno Castro da Silva; Ana L. C. Bazzan (AAMAS, 2017)

Task-based behavior generalization via manifold clustering. - Rafael Garcia; Bruno Castro da Silva; João Luiz Dihl Comba (IROS, 2017)

On Ensuring that Intelligent Machines Are Well-Behaved. - Philip S. Thomas; Bruno Castro da Silva; Andrew G. Barto; Emma Brunskill (CoRR, 2017)

Identifying Reusable Macros for Efficient Exploration via Policy Compression. - Francisco M. Garcia; Bruno C. da Silva 001 (CoRR, 2017)

Towards Designing Optimal Reward Functions in Multi-Agent Reinforcement Learning Problems. - Ricardo Grunitzki; Bruno Castro da Silva; Ana L. C. Bazzan (IJCNN, 2018)

Comparing Multi-Armed Bandit Algorithms and Q-learning for Multiagent Action Selection: a Case Study in Route Choice. - Thiago Bell Felix de Oliveira; Ana L. C. Bazzan; Bruno C. da Silva 001; Ricardo Grunitzki (IJCNN, 2018)

Efficient Local Memory Support for Approximate Computing. - Marcelo Brandalero; Guilherme Meneguzzi Malfatti; Geraldo Francisco Oliveira; Leonardo Almeida da Silveira; Larissa Rozales Gonçalves; Bruno Castro da Silva; Luigi Carro; Antonio Carlos Schneider Beck (SBESC, 2018)

A task-and-technique centered survey on visual analytics for deep learning model engineering. - Rafael Garcia; Alexandru C. Telea; Bruno Castro da Silva; Jim Tørresen; João Luiz Dihl Comba (Comput. Graph., 2018)

A Compression-Inspired Framework for Macro Discovery. - Francisco M. Garcia; Bruno C. da Silva 001; Philip S. Thomas (AAMAS, 2019)

Identifying Reusable Early-Life Options. - Aline Weber; Charles P. Martin; Jim Tørresen; Bruno C. da Silva 001 (ICDL-EPIROB, 2019)

A Methodology for Neural Network Architectural Tuning Using Activation Occurrence Maps. - Rafael Garcia; Alexandre Xavier Falcão; Alexandru C. Telea; Bruno Castro da Silva; Jim Tørresen; João Luiz Dihl Comba (IJCNN, 2019)

Parameterized Melody Generation with Autoencoders and Temporally-Consistent Noise. - Aline Weber; Lucas Nunes Alegre; Jim Tørresen; Bruno C. da Silva 001 (NIME, 2019)

Autonomous Open-Ended Learning of Interdependent Tasks. - Vieri Giuliano Santucci; Emilio Cartoni; Bruno Castro da Silva; Gianluca Baldassarre (CoRR, 2019)

Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints. - Manuel Del Verme; Bruno Castro da Silva; Gianluca Baldassarre (CoRR, 2020)

Autonomous learning of multiple, context-dependent tasks. - Vieri Giuliano Santucci; Davide Montella; Bruno Castro da Silva; Gianluca Baldassarre (CoRR, 2020)

Data clustering for efficient approximate computing. - Michael G. Jordan; Marcelo Brandalero; Guilherme Meneguzzi Malfatti; Geraldo F. Oliveira; Arthur Francisco Lorenzon; Bruno C. da Silva 001; Luigi Carro; Mateus B. Rutzig; Antonio Carlos Schneider Beck (Des. Autom. Embed. Syst., 2020)

Toll-based reinforcement learning for efficient equilibria in route choice. - Gabriel de Oliveira Ramos; Bruno C. da Silva 001; Roxana Radulescu; Ana L. C. Bazzan; Ann Nowé (Knowl. Eng. Rev., 2020)

Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection. - Lucas Nunes Alegre; Ana L. C. Bazzan; Bruno C. da Silva 001 (AAMAS, 2021)

Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods. - Chris Nota; Philip S. Thomas; Bruno C. da Silva 001 (ICML, 2021)

Universal Off-Policy Evaluation. - Yash Chandak; Scott Niekum; Bruno C. da Silva 001; Erik G. Learned-Miller; Emma Brunskill; Philip S. Thomas (NeurIPS, 2021)

Patterns of high-risk drinking among medical students: A web-based survey with machine learning. - Grasiela Marcon; Flávia de Ávila Pereira; Aline Zimerman; Bruno Castro da Silva; Lisia von Diemen; Ives Cavalcante Passos; Mariana Recamonde Mendoza (Comput. Biol. Medicine, 2021)

Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control. - Lucas Nunes Alegre; Ana L. C. Bazzan; Bruno C. da Silva 001 (PeerJ Comput. Sci., 2021)

RADAR: Reactive and Deliberative Adaptive Reasoning - Learning When to Think Fast and When to Think Slow. - Ørjan Strand; Didrik Spanne Reilstad; Zhenying Wu; Bruno Castro da Silva; Jim Torresen; Kai Olav Ellefsen (ICDL, 2022)

Fairness Guarantees under Demographic Shift. - Stephen Giguere 001; Blossom Metevier; Bruno Castro da Silva; Yuriy Brun; Philip S. Thomas; Scott Niekum (ICLR, 2022)

Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer. - Lucas Nunes Alegre; Ana L. C. Bazzan; Bruno C. da Silva 001 (ICML, 2022)

Constrained Offline Policy Optimization. - Nicholas Polosky; Bruno C. da Silva 001; Madalina Fiterau; Jithin Jagannath (ICML, 2022)

Look-Ahead Reinforcement Learning for Load Balancing Network Traffic. - Isadora P. Possebon; Bruno Castro da Silva; Alberto E. Schaeffer-Filho (ISCC, 2022)

Off-Policy Evaluation for Action-Dependent Non-stationary Environments. - Yash Chandak; Shiv Shankar; Nathaniel D. Bastian; Bruno C. da Silva 001; Emma Brunskill; Philip S. Thomas (NeurIPS, 2022)

Enforcing Delayed-Impact Fairness Guarantees. - Aline Weber; Blossom Metevier; Yuriy Brun; Philip S. Thomas; Bruno Castro da Silva (CoRR, 2022)

Model-Based Reinforcement Learning with SINDy. - Rushiv Arora; Bruno Castro da Silva; Eliot Moss (CoRR, 2022)

Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization. - Lucas Nunes Alegre; Ana L. C. Bazzan; Diederik M. Roijers; Ann Nowé; Bruno C. da Silva 001 (AAMAS, 2023)

Seldonian Toolkit: Building Software with Safe and Fair Machine Learning. - Austin Hoag; James E. Kostas; Bruno Castro da Silva; Philip S. Thomas; Yuriy Brun (ICSE Companion, 2023)

Multi-Step Generalized Policy Improvement by Leveraging Approximate Models. - Lucas Nunes Alegre; Ana L. C. Bazzan; Ann Nowé; Bruno C. da Silva 001 (NeurIPS, 2023)

A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. - Florian Felten; Lucas N. Alegre; Ann Nowé; Ana L. C. Bazzan; El-Ghazali Talbi; Grégoire Danoy; Bruno C. da Silva 001 (NeurIPS, 2023)

Behavior Alignment via Reward Function Optimization. - Dhawal Gupta; Yash Chandak; Scott M. Jordan; Philip S. Thomas; Bruno C. da Silva 001 (NeurIPS, 2023)

Coagent Networks: Generalized and Scaled. - James E. Kostas; Scott M. Jordan; Yash Chandak; Georgios Theocharous; Dhawal Gupta; Martha White; Bruno Castro da Silva; Philip S. Thomas (CoRR, 2023)

From Past to Future: Rethinking Eligibility Traces. - Dhawal Gupta; Scott M. Jordan; Shreyas Chaudhari; Bo Liu 006; Philip S. Thomas; Bruno Castro da Silva (AAAI, 2024)

Position: Benchmarking is Limited in Reinforcement Learning Research. - Scott M. Jordan; Adam White 001; Bruno Castro da Silva; Martha White; Philip S. Thomas (ICML, 2024)

Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation. - Shreyas Chaudhari; Ameet Deshpande; Bruno C. da Silva 001; Philip S. Thomas (NeurIPS, 2024)

Mitigating the Curse of Horizon in Monte-Carlo Returns. - Alex Ayoub; David Szepesvari; Francesco Zanini; Bryan Chan; Dhawal Gupta; Bruno Castro da Silva; Dale Schuurmans (RLJ, 2024)

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs. - Shreyas Chaudhari; Pranjal Aggarwal; Vishvak Murahari; Tanmay Rajpurohit; Ashwin Kalyan; Karthik Narasimhan; Ameet Deshpande; Bruno Castro da Silva (CoRR, 2024)

Dynamic Option Creation in Option-Critic Reinforcement Learning. - Mateus Begnini Melchiades; Gabriel de Oliveira Ramos; Bruno C. da Silva 001 (AAMAS, 2025)

Successor Clusters: A Behavior Basis for Unsupervised Zero-Shot Reinforcement Learning. - Louis Bagot; Lucas Nunes Alegre; Steven Latré; Kevin Mets; Bruno Castro da Silva (Trans. Mach. Learn. Res., 2025)

Constructing an Optimal Behavior Basis for the Option Keyboard. - Lucas N. Alegre; Ana L. C. Bazzan; André Barreto 001; Bruno C. da Silva 001 (CoRR, 2025)