optimizing reservoir operations with reinforcement learning: a data-driven framework

Effective reservoir management demands adaptive, data-driven strategies to optimize storage and release decisions while balancing multiple, often competing, operational objectives. this study investigates the application of q-learning, a model-free reinforcement learning (rl) algorithm, for optimizing reservoir releases under dynamic and uncertain hydrological conditions. unlike conventional rule-based or offline optimization methods, the proposed rl approach continuously refines its release policy by learning from environmental feedback and observed states, enabling real-time adaptation without the need for a predefined system model. the framework is tested on the dez reservoir in iran, a real-world case study characterized by significant inflow variability and seasonal water demand. simulation results demonstrate that q-learning effectively manages operational complexity, maintaining storage within prescribed bounds and delivering release patterns closely aligned with demand. to benchmark performance, a simplified ant colony optimization (aco) model is implemented for comparison. while aco shows moderate capability in deficit reduction, q-learning outperforms it in terms of constraint satisfaction and long-term feasibility. findings highlight the strong potential of reinforcement learning to support intelligent, scalable, and robust decision-making in modern reservoir operation systems under uncertainty.