用多智能体强化学习优化气候政策，解决复杂系统与多方利益难题
Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis
- 将多智能体强化学习融入气候模拟，自动寻找政策路径
- 克服非线性动态与不确定性传播带来的建模挑战
- 适合政策研究者和气候建模团队参考，推动智能决策
气候政策制定面临深度不确定性、系统动态复杂性和利益相关方冲突等挑战。尽管地球系统模型已成为政策探索的重要工具，但通常仅用于评估政策方案，而非直接生成政策。可将问题反向为优化政策路径，但传统方法难以应对非线性动力学、异质主体及全面的不确定性量化。本文提出一种将多智能体强化学习（MARL）融入气候模拟的框架，以应对这些局限。识别了在气候模拟与政策合成结合中关键挑战：奖励函数设计、智能体与状态空间扩展的可扩展性、跨系统不确定性传播，以及解的验证。同时探讨了如何使MARL生成方案对决策者更具可解释性与实用性。该框架为更复杂的气候政策探索提供基础，并指明重要局限与未来研究方向。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。