Climate policy development faces significant challenges due to deep uncertainty, complex system dynamics, and competing stakeholder interests. Climate simulation methods, such as Earth System Models, have become valuable tools for policy exploration. However, their typical use is for evaluating potential polices, rather than directly synthesizing them. The problem can be inverted to optimize for policy pathways, but the traditional optimization approaches often struggle with non-linear dynamics, heterogeneous agents, and comprehensive uncertainty quantification. We propose a framework for augmenting climate simulations with Multi-Agent Reinforcement Learning (MARL) to address these limitations. We identify key challenges at the interface between climate simulations and the application of MARL in the context of policy synthesis, including reward definition, scalability with increasing agents and state spaces, uncertainty propagation across linked systems, and solution validation. Additionally, we discuss challenges in making MARL-derived solutions interpretable and useful for policy-makers. Our framework provides a foundation for more sophisticated climate policy exploration while acknowledging important limitations and areas for future research.

翻译：气候政策制定面临着深度不确定性、复杂系统动态以及利益相关者竞争等多重挑战。以地球系统模型为代表的气候模拟方法已成为政策探索的重要工具。然而，这些模型通常用于评估潜在政策，而非直接生成政策方案。虽然可以通过逆向优化来寻求政策路径，但传统优化方法在处理非线性动态、异质智能体及综合不确定性量化方面存在明显局限。本文提出一种将多智能体强化学习与气候模拟相结合的框架以应对这些挑战。我们系统阐述了在政策综合背景下，气候模拟与MARL方法融合面临的核心难题，包括奖励函数定义、智能体数量与状态空间扩展的可扩展性、关联系统间的不确定性传递以及解决方案验证等问题。同时，我们深入探讨了如何使MARL生成的解决方案对政策制定者更具可解释性与实用性。本框架为开展更深入的气候政策探索奠定了基础，同时也明确了当前方法的局限性与未来研究方向。