mlsanityData to 14 Oct 2026

Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen

arXiv 2409.18574 ↗First posted 27 Sep 2024cs.LGcited by 2

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

Abstract (arXiv)

Due to climate change the frequency and intensity of extreme rainfall events, which contribute to urban flooding, are expected to increase in many places. These floods can damage transport infrastructure and disrupt mobility, highlighting the need for cities to adapt to escalating risks. Reinforcement learning (RL) serves as a powerful tool for uncovering optimal adaptation strategies, determining how and where to deploy adaptation measures effectively, even under significant uncertainty. In this study, we leverage RL to identify the most effective timing and locations for implementing measures, aiming to reduce both direct and indirect impacts of flooding. Our framework integrates climate change projections of future rainfall events and floods, models city-wide motorized trips, and quantifies direct and indirect impacts on infrastructure and mobility. Preliminary results suggest that our RL-based approach can significantly enhance decision-making by prioritizing interventions in specific urban areas and identifying the optimal periods for their implementation. Our framework is publicly available: urlhttps://github.com/MLSM-at-DTU/floodstransportrl.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 38 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

2 of 38 references.

References · 2

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Diverse Planning with Simulators via Linear Temporal Logiccited by 1
  2. 2026Metalearning traffic assignment for network disruptions with graph convolutional neural networkscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport
  2. 2026Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning
  3. 2025Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning
  4. 2025Climate Adaptation with Reinforcement Learning: Economic vs. Quality of Life Adaptation Pathways
  5. 2025Using Reinforcement Learning to Integrate Subjective Wellbeing into Climate Adaptation Decision Making
  6. 2024RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
  7. 2025Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control
  8. 2024Reinforcement Learning for Sociohydrology