Conventional computational models of climate adaptation frameworks inadequately consider decision-makers’ capacity to learn, update, and improve decisions. Here, we investigate the potential of reinforcement learning (RL), a machine learning technique that efficaciously acquires knowledge from the environment and systematically optimizes dynamic decisions, in modeling and informing adaptive climate decision-making. We consider coastal flood risk mitigations for Manhattan, New York City, USA (NYC), illustrating the benefit of continuously incorporating observations of sea-level rise into systematic designs of adaptive strategies. We find that when designing adaptive seawalls to protect NYC, the RL-derived strategy significantly reduces the expected net cost by 6 to 36% under the moderate emissions scenario SSP2-4.5 (9 to 77% under the high emissions scenario SSP5-8.5), compared to conventional methods. When considering multiple adaptive policies, including accomodation and retreat as well as protection, the RL approach leads to a further 5% (15%) cost reduction, showing RL’s flexibility in coordinatively addressing complex policy design problems. RL also outperforms conventional methods in controlling tail risk (i.e., low probability, high impact outcomes) and in avoiding losses induced by misinformation about the climate state (e.g., deep uncertainty), demonstrating the importance of systematic learning and updating in addressing extremes and uncertainties related to climate adaptation.

中文翻译：

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基于强化学习的气候变化适应适应性策略：沿海洪水风险管理的应用


气候适应框架的传统计算模型没有充分考虑决策者学习、更新和改进决策的能力。在这里，我们研究了强化学习 （RL） 的潜力，这是一种机器学习技术，可以有效地从环境中获取知识并系统地优化动态决策，在建模和告知适应性气候决策方面。我们考虑了美国纽约市曼哈顿 （NYC） 的沿海洪水风险缓解措施，说明了不断将海平面上升的观测结果纳入适应性策略的系统设计中的好处。我们发现，在设计自适应海堤以保护纽约市时，与传统方法相比，在中等排放情景 SSP2-4.5 下，RL 衍生的策略将预期净成本显著降低了 6% 至 36%（在高排放情景 SSP5-8.5 下降低了 9% 至 77%）。当考虑多种适应性策略时，包括住宿和撤退以及保护，RL 方法可进一步降低 5% （15%） 的成本，显示了 RL 在协调解决复杂策略设计问题方面的灵活性。RL 在控制尾部风险（即低概率、高影响结果）和避免因气候状态的错误信息（例如深度不确定性）而造成的损失方面也优于传统方法，证明了系统学习和更新在解决与气候适应相关的极端和不确定性方面的重要性。