用强化学习优化城市防洪策略，提升长期生活质量。
Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning
- 基于强化学习，整合降雨、洪水、交通与生活质量模型。
- 相比现实规划方案，该方法显著提升长期生活质量指数。
- 适合关注气候适应与智能决策的城市规划者使用。
随着气候变化，城市内涝预计将更加频繁和严重，严重影响城市生活质量（QoL）。政策制定者需应对气候不确定性和城市内涝的复杂动态性。强化学习（RL）在处理此类复杂、动态且不确定问题方面具有巨大潜力。本文利用集成评估模型（IAM），结合降雨预测模型、洪水模型、交通可达性模型和生活质量指数，通过强化学习识别能带来更高长期生活质量的气候适应路径。初步结果表明，该方法可学习最优适应措施，其表现优于其他现实可行的规划策略。框架已开源：https://github.com/MLSM-at-DTU/maat_qol_framework。
原文摘要 · Abstract (English)
Urban flooding is expected to increase in frequency and severity as a consequence of climate change, causing wide-ranging impacts that include a decrease in urban Quality of Life (QoL). Meanwhile, policymakers must devise adaptation strategies that can cope with the uncertain nature of climate change and the complex and dynamic nature of urban flooding. Reinforcement Learning (RL) holds significant promise in tackling such complex, dynamic, and uncertain problems. Because of this, we use RL to identify which climate adaptation pathways lead to a higher QoL in the long term. We do this using an Integrated Assessment Model (IAM) which combines a rainfall projection model, a flood model, a transport accessibility model, and a quality of life index. Our preliminary results suggest that this approach can be used to learn optimal adaptation measures and it outperforms other realistic and real-world planning strategies. Our framework is publicly available: https://github.com/MLSM-at-DTU/maat_qol_framework.
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