activity
20232026
most citedSMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks

3 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

physics.soc-ph2026

Ant swarm functional control via stigmergic Reinforcement Learning agents

Alessio Pitteri, Andrea Guizzo, Laura Ferrarotti +2

In this work, we propose a novel framework for the functional controllability of the ant swarm model, a well-known and relevant model of collective behaviour. Our approach introduc…

cs.AI20261 cited

Generative AI collective behavior needs an interactionist paradigm

Laura Ferrarotti, Gian Maria Campedelli, Roberto Dessì +7

In this article, we argue that understanding the collective behavior of agents based on large language models (LLMs) is an essential area of inquiry, with important implications in…

physics.soc-ph2024

Predicting human cooperation: sensitizing drift-diffusion model to interaction and external stimuli

Lucila G. Alvarez-Zuzek, Laura Ferrarotti, Bruno Lepri +1

As humans perceive and actively engage with the world, we adjust our decisions in response to shifting group dynamics and are influenced by social interactions. This study aims to…

cs.LG20243 cited

SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks

Mátyás Vincze, Laura Ferrarotti, Leonardo Lucio Custode +2

Continuous control tasks often involve high-dimensional, dynamic, and non-linear environments. State-of-the-art performance in these tasks is achieved through complex closed-box po…

cs.AI2023

Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation

Laura Ferrarotti, Massimiliano Luca, Gabriele Santin +9

Optimizing traffic dynamics in an evolving transportation landscape is crucial, particularly in scenarios where autonomous vehicles (AVs) with varying levels of autonomy coexist wi…