4 papers
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…
Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations
James Rudd-Jones, Fiona Thendean, MarÃa 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…
Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis
James Rudd-Jones, Mirco Musolesi, MarÃa 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…