23 papers
TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning
Charles Westphal, Stephen Hailes, Mirco Musolesi
Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL). These variables must efficiently capture the information n…
GenTract: Generative Global Tractography
Alec Sargood, Lemuel Puglisi, Elinor Thompson +2
Tractography is the process of inferring the trajectories of white-matter pathways in the brain from diffusion magnetic resonance imaging (dMRI). Local tractography methods, which…
On Distributional Reinforcement Learning in Chaotic Dynamical Systems
James Rudd-Jones, Mirco Musolesi, MarÃa Pérez-Ortiz
Chaotic dynamical systems pose a fundamental challenge for Reinforcement Learning (RL): exponential sensitivity to initial conditions induces high-variance bootstrap targets and po…
An Agent-Centric Dynamical Systems Perspective on Multi-Agent Reinforcement Learning
James Rudd-Jones, MarÃa Pérez-Ortiz, Mirco Musolesi
Analysing learning in Multi-Agent Reinforcement Learning (MARL) environments is challenging, in particular with respect to \textit{individual} decision-making. Practitioners freque…
Emergent Semantic Role Understanding in Language Models
Carla Griffiths, Mirco Musolesi
Understanding how linguistic structure emerges in language models is central to interpreting what these systems learn from data and how much supervision they truly require. In part…
On the Creativity of AI Agents
Giorgio Franceschelli, Mirco Musolesi
Large language models (LLMs), particularly when integrated into agentic systems, have demonstrated human- and even superhuman-level performance across multiple domains. Whether the…