serio agriesti

Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM

Climate policy analysis requires models that capture multi-gas climate effects, but such models are too slow to embed in reinforcement learning loops at scale. In collaboration with a pan-European public-sector environmental agency, we develop a multi-agent reinforcement learning (MARL) framework th