Reinforcement Learning for Sociohydrology
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
In this study, we discuss how reinforcement learning (RL) provides an effective and efficient framework for solving sociohydrology problems. The efficacy of RL for these types of problems is evident because of its ability to update policies in an iterative manner - something that is also foundational to sociohydrology, where we are interested in representing the co-evolution of human-water interactions. We present a simple case study to demonstrate the implementation of RL in a problem of runoff reduction through management decisions related to changes in land-use land-cover (LULC). We then discuss the benefits of RL for these types of problems and share our perspectives on the future research directions in this area.
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 1 of its 7 references that are in the map. First posted on arXiv 31 May 2024.
Builds on
1 of 7 references.
References · 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Multi-Objective Reinforcement Learning for Water Management
- 2024Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations
- 2024Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen
- 2025BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management
- 2025Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning
- 2024Mapping Social Choice Theory to RLHF
- 2024A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback
- 2026Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport