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

arXiv: https://arxiv.org/abs/2510.07971 · HTML: https://mlsanity.com/paper/2510.07971/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks](https://mlsanity.com/paper/2006.07869/) (2020)
- [AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N](https://mlsanity.com/paper/2208.07004/) (2022)
- [TOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching](https://mlsanity.com/paper/2305.12663/) (2023)
- [Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques](https://mlsanity.com/paper/1901.05125/) (2019)
- [Deep reinforcement learning in World-Earth system models to discover sustainable management strategies](https://mlsanity.com/paper/1908.05567/) (2019)
- [Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis](https://mlsanity.com/paper/2504.12777/) (2025)
- [Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2505.01115/) (2025)
- [Improving International Climate Policy via Mutually Conditional Binding Commitments](https://mlsanity.com/paper/2307.14267/) (2023)

## Abstract (arXiv)

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 the European Environment 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.