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

Bohn Lassen, Oskar; Agriesti, Serio; Rodrigues, Filipe; Kurnik, Blaz; Pereira, Francisco

doi:10.24963/ijcai.2026/788

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

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 7084-7092.

https://doi.org/10.24963/ijcai.2026/788

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

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.

Oskar Bohn Lassen, Serio Agriesti, Filipe Rodrigues, Blaz Kurnik, and Francisco Pereira. 2026. Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 7084–7092. https://doi.org/10.24963/ijcai.2026/788

@inproceedings{ijcai2026p788,
  title     = {Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM},
  author    = {Bohn Lassen, Oskar and Agriesti, Serio and Rodrigues, Filipe and Kurnik, Blaz and Pereira, Francisco},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {7084--7092},
  year      = {2026},
  month     = {8},
  note      = {AI and Social Good},
  doi       = {10.24963/ijcai.2026/788},
  url       = {https://doi.org/10.24963/ijcai.2026/788},
}

TY  - CPAPER
TI  - Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM
AU  - Bohn Lassen, Oskar
AU  - Agriesti, Serio
AU  - Rodrigues, Filipe
AU  - Kurnik, Blaz
AU  - Pereira, Francisco
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 7084
EP  - 7092
DO  - 10.24963/ijcai.2026/788
UR  - https://doi.org/10.24963/ijcai.2026/788
ER  -