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

Oskar Bohn Lassen; serio agriesti; Filipe Rodrigues; Blaz Kurnik; Francisco Pereira

IJCAI 20260 citations

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

Oskar Bohn Lassen, serio agriesti, Filipe Rodrigues, Blaz Kurnik, Francisco Pereira

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 high-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 0.0004 with 1000x faster one-step inference and yields 100x 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.

Agent-based and Multi-agent Systems: Agent-based and Multi-agent SystemsHumans and AI: Humans and AIMachine Learning: Machine Learning

BibTeX

@inproceedings{ijcai2026_climatesurrogate,
  title = {Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM},
  author = {Oskar Bohn Lassen and serio agriesti and Filipe Rodrigues and Blaz Kurnik and Francisco Pereira},
  booktitle = {IJCAI 2026},
  year = {2026}
}