2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts
Marius Captari, Remo Sasso, Matthia Sabatelli
Despite the considerable attention given to the questions of \textit{how much} and \textit{how to} explore in deep reinforcement learning, the investigation into \textit{when} to e…
cs.LG2023★ 2 cited
Posterior Sampling for Deep Reinforcement Learning
Remo Sasso, Michelangelo Conserva, Paulo Rauber
Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-base…