{"type":"ScholarlyArticle","title":"Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM","authors":[{"name":"Oskar Bohn Lassen","slug":"oskar-bohn-lassen","given":"Oskar","family":"Bohn Lassen","affiliation":""},{"name":"Serio Agriesti","slug":"serio-agriesti","given":"Serio","family":"Agriesti","affiliation":""},{"name":"Filipe Rodrigues","slug":"filipe-rodrigues","given":"Filipe","family":"Rodrigues","affiliation":""},{"name":"Blaz Kurnik","slug":"blaz-kurnik","given":"Blaz","family":"Kurnik","affiliation":""},{"name":"Francisco Pereira","slug":"francisco-pereira","given":"Francisco","family":"Pereira","affiliation":""}],"abstract":"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 that integrates a higher-fidelity climate surrogate as the environment transition, enabling regional agents to learn policies under multi-gas dynamics. We train a recurrent surrogate on 20,000 multi-gas emission pathways to emulate CICERO-SCM. The surrogate achieves near-simulator accuracy (global-mean temperature RMSE ≈ 4 × 10⁻⁴ K) with ∼1000× faster one-step inference and yields >100× end-to-end MARL training speed-up. We show policy agreement with the simulator in tractable settings and propose a replay- and rank-consistency test (Kendall’s τ) for assessing policy fidelity when simulator-in-the-loop training is infeasible. This enables large-scale multi-agent policy experiments while retaining high-fidelity multi-gas climate response.","doi":"10.24963/ijcai.2026/788","url":"https://ijcai.org/proceedings/2026/788","pdf":"https://ijcai.org/proceedings/2026/0788.pdf","markdown":"https://ijcai.org/proceedings/2026/788.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/788","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":788,"pages":{"first":7084,"last":7092},"keywords":["Machine Learning: Machine Learning","AI Ethics, Trust, Fairness: AI Ethics, Trust, Fairness","Agent-based and Multi-agent Systems: Agent-based and Multi-agent Systems","Humans and AI: Humans and AI"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}