4 papers
On Distributional Reinforcement Learning in Chaotic Dynamical Systems
James Rudd-Jones, Mirco Musolesi, María 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, María 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…
Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis
James Rudd-Jones, Mirco Musolesi, María Pérez-Ortiz
Climate policy development faces significant challenges due to deep uncertainty, complex system dynamics, and competing stakeholder interests. Climate simulation methods, such as E…
Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations
James Rudd-Jones, Fiona Thendean, María Pérez-Ortiz
Climate change poses an existential threat, necessitating effective climate policies to enact impactful change. Decisions in this domain are incredibly complex, involving conflicti…