6 citations · 11 across the 8 of their papers we have counts for
8 papers
Exploration with Foundation Models: Capabilities, Limitations, and Hybrid Approaches
Remo Sasso, Michelangelo Conserva, Dominik Jeurissen +1
Exploration in reinforcement learning (RL) remains challenging, particularly in sparse-reward settings. While foundation models possess strong semantic priors, their capabilities a…
On the Limits of Tabular Hardness Metrics for Deep RL: A Study with the Pharos Benchmark
Michelangelo Conserva, Remo Sasso, Paulo Rauber
Principled evaluation is critical for progress in deep reinforcement learning (RL), yet it lags behind the theory-driven benchmarks of tabular RL. While tabular settings benefit fr…
Foundation Models as World Models: A Foundational Study in Text-Based GridWorlds
Remo Sasso, Michelangelo Conserva, Dominik Jeurissen +1
While reinforcement learning from scratch has shown impressive results in solving sequential decision-making tasks with efficient simulators, real-world applications with expensive…
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…
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…
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task. Transfer learning proposes…
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks. Recent progress in model-based RL allows age…
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains
Hamidreza Kasaei, Sha Luo, Remo Sasso +1
To aid humans in everyday tasks, robots need to know which objects exist in the scene, where they are, and how to grasp and manipulate them in different situations. Therefore, obje…