• Integrated hydrologic simulation models of water systems into the RL-based optimization framework • Established a custom environment to present a virtual platform for RL agent to explore and learn to address conjunctive water use challenge. • Designed proper reward functions for measuring RL agent’s actions. • Leveraged reinforcement learning algorithms for seeking efficient conjunctive water use policy