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

A Reinforcement Learning Approach with Fourier Basis Linear Function Approximation for Traffic Signal Control. - Theresa Ziemke; Lucas Nunes Alegre; Ana L. C. Bazzan (ATT@ECAI, 2020)

SelfieArt: Interactive Multi-Style Transfer for Selfies and Videos with Soft Transitions. - Lucas Nunes Alegre; Manuel M. Oliveira (SIBGRAPI, 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)

Reinforcement learning vs. rule-based adaptive traffic signal control: A Fourier basis linear function approximation for traffic signal control. - Theresa Ziemke; Lucas Nunes Alegre; Ana L. C. Bazzan (AI Commun., 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)

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)

On the Explainability and Expressiveness of Function Approximation Methods in RL-Based Traffic Signal Control. - Lincoln Vinicius Schreiber; Lucas Nunes Alegre; Ana L. C. Bazzan; Gabriel de Oliveira Ramos (IJCNN, 2022)

Using Reinforcement Learning to Control Traffic Signals in a Real-World Scenario: An Approach Based on Linear Function Approximation. - Lucas Nunes Alegre; Theresa Ziemke; Ana L. C. Bazzan (IEEE Trans. Intell. Transp. Syst., 2022)

Towards Sample-Efficient Multi-Objective Reinforcement Learning. - Lucas Nunes Alegre (AAMAS, 2023)

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)

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)

Knowledge transfer in multi-objective multi-agent reinforcement learning via generalized policy improvement. - Vicente Nejar de Almeida; Lucas N. Alegre; Ana L. C. Bazzan (Comput. Sci. Inf. Syst., 2024)

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning. - Shengyi Huang; Quentin Gallouédec; Florian Felten; Antonin Raffin; Rousslan Fernand Julien Dossa; Yanxiao Zhao; Ryan Sullivan; Viktor Makoviychuk; Denys Makoviichuk; Mohamad H. Danesh; Cyril Roumégous; Jiayi Weng; et al. (CoRR, 2024)

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)

AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning. - Lucas N. Alegre; Agon Serifi; Ruben Grandia; David Müller 011; Espen Knoop; Moritz Bächer (CoRR, 2025)