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.

通过一个关于土地利用变化下径流减少问题的简单案例研究，我们讨论了强化学习在解决社会水文问题中提供了一种有效且高效的框架。强化学习之所以在这类问题中具有功效，是因为它能够以迭代方式更新策略，而这也是社会水文学的基础，我们关注的是人水互动的共同演化。我们还讨论了强化学习在这类问题中的好处，并分享了对未来研究方向的观点。