Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospects

Urban drainage systems (UDSs) are vital for managing stormwater and wastewater but face growing challenges due to urbanization, climate change and aging infrastructure. Real-time control (RTC) enhances UDSs' performance and circumvents the need for system upgrades through adaptive management and repurposing existing systems. Meanwhile, deep reinforcement learning (DRL) has emerged as a promising tool to improve decision-making, stability in the dynamic, nonlinear and dimensional environments. Recent studies demonstrate the potential of deep reinforcement learning control (DRLC) in flood mitigation, sewer overflow reduction, water quality management, and wastewater treatment optimization. While DRLC offers transformative opportunities for UDSs control optimization, its widespread adoption and real-world implementation requires long-term effort to address technical and practical gaps. This review systematically evaluates DRLC's progress in UDSs, summarizes the critical limitations, and proposes constructive insights, including data management, surrogate model design, benchmark frameworks construction, interpretability, safe control frameworks, and UDSs resilience enhancement to advance its future research.