6 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…
Do machine learning climate models work in changing climate dynamics?
Maria Conchita Agana Navarro, Geng Li, Theo Wolf +1
Climate change is accelerating the frequency and severity of unprecedented events, deviating from established patterns. Predicting these out-of-distribution (OOD) events is critica…
Are Large Language Models Strategic Decision Makers? A Study of Performance and Bias in Two-Player Non-Zero-Sum Games
Nathan Herr, Fernando Acero, Roberta Raileanu +2
Large Language Models (LLMs) have been increasingly used in real-world settings, yet their strategic decision-making abilities remain largely unexplored. To fully benefit from the…