In this study, a cyber-physical system (CPS) for river flow path control is proposed using reinforcement learning. Recently, there has been a frequent occurrence of river flooding due to heavy rains, resulting in serious economic losses and victims. One cause of river flooding is the meandering due to the river bed growing and flow path change. As a mean of avoiding the meandering, river groynes can be used to regularize the flow. However, the mechanism of the flow path growing, and its optimal control is unclear. Therefore, in this study, a dynamic flow path control system is proposed using a data-driven approach to solve the problem at once. As a data-driven approach, reinforcement learning is adopted. The proposed system is designed to control meandering by adaptively deforming and moving the groynes with the reward of the flow path health. The effectiveness of the proposed flow path control system is verified through a simulation of the river model.