mlsanityData to 14 Oct 2026

Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning

arXiv 2511.03238 ↗First posted 5 Nov 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 20 references that are in the map. First posted on arXiv 5 Nov 2025.

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3 of 20 references.

References · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171
  3. 2022Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systemscited by 12

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  4. 2026Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning
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  6. 2024RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
  7. 2024Reinforcement Learning for Sociohydrology
  8. 2025Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control