  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.
