Reinforcement learning applications in water resource management: a systematic literature review - Kåge, Linus; Milić, Vlatko; Andersson, Maria; Wallén, Magnus (Frontiers in Water, 2025)
Climate change is increasingly affecting the water cycle, with droughts and floods posing significant challenges for agriculture, hydropower production, and urban water resource management due to growing variability in the factors influencing the water cycle. Reinforcement learning (RL) has demonstrated promising potential in optimization and planning tasks, as it trains models on historical data or through simulations, allowing them to generate new data by interacting with the simulator. This systematic literature review examines the application of reinforcement learning (RL) in water resource management across various domains. A total of 40 articles were analyzed, revealing that RL is a viable approach for this field due to its capability to learn and optimize sequential decision-making processes. The results show that RL agents are primarily trained in simulated environments rather than directly on historical data. Among the algorithms, deep Q-networks are the most commonly employed. Future research should address the challenges of bridging the gap between simulation and real-world applications and focus on improving the explainability of the decision-making process. Future studies need to address the challenges of bridging the gap between simulation and real-world applications. Furthermore, future research should focus on the explainability behind the decision-making process of the agent, which is important due to the safety-critical nature of the application.