A new convergent variant of Q-learning with linear function approximation. - Diogo S. Carvalho; Francisco S. Melo; Pedro Santos 001 (NeurIPS, 2020)

CHARET: Character-centered Approach to Emotion Tracking in Stories. - Diogo S. Carvalho; Joana Campos 001; Manuel Guimarães; Ana Antunes; João Dias 001; Pedro A. Santos 001 (AAMAS, 2021)

Understanding the Impact of Data Distribution on Q-learning with Function Approximation. - Pedro P. Santos; Francisco S. Melo; Alberto Sardinha; Diogo S. Carvalho (CoRR, 2021)

Hierarchically Structured Scheduling and Execution of Tasks in a Multi-agent Environment. - Diogo S. Carvalho; Biswa Sengupta (EPIA, 2022)

Theoretical Remarks on Feudal Hierarchies and Reinforcement Learning. - Diogo S. Carvalho; Francisco S. Melo; Pedro A. Santos 001 (ECAI, 2023)

Multi-Bellman operator for convergence of Q-learning with linear function approximation. - Diogo S. Carvalho; Pedro A. Santos 001; Francisco S. Melo (CoRR, 2023)

Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning. - Pedro P. Santos; Diogo S. Carvalho; Miguel Vasco; Alberto Sardinha; Pedro A. Santos 001; Ana Paiva 001; Francisco S. Melo (AAMAS, 2024)

The impact of data distribution on Q-learning with function approximation. - Pedro P. Santos; Diogo S. Carvalho; Alberto Sardinha; Francisco S. Melo (Mach. Learn., 2024)

Reinforcement learning in convergently non-stationary environments: Feudal hierarchies and learned representations. - Diogo S. Carvalho; Pedro A. Santos 001; Francisco S. Melo (Artif. Intell., 2025)

Multi-Bellman operator for convergence of Q-learning with linear function approximation. - Diogo S. Carvalho; Pedro A. Santos 001; Francisco S. Melo (Trans. Mach. Learn. Res., 2025)