3 papers
cs.LG2026
K-Myriad: Jump-starting reinforcement learning with unsupervised parallel agents
Vincenzo De Paola, Mirco Mutti, Riccardo Zamboni +1
Parallelization in Reinforcement Learning is typically employed to speed up the training of a single policy, where multiple workers collect experience from an identical sampling di…
cs.LG2025
Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
Davide Salaorni, Vincenzo De Paola, Samuele Delpero +9
In recent years, \emph{Reinforcement Learning} (RL) has made remarkable progress, achieving superhuman performance in a wide range of simulated environments. As research moves towa…
cs.LG2025
Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story
Vincenzo De Paola, Riccardo Zamboni, Mirco Mutti +1
Parallel data collection has redefined Reinforcement Learning (RL), unlocking unprecedented efficiency and powering breakthroughs in large-scale real-world applications. In this pa…