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Efficient reinforcement learning for urban drainage control via a neural network-based model using truncated and

Zhiyu Zhang¹, Wenchong Tian², Zhenliang Liao³

¹School of Energy and Environment, City University of Hong Kong, Hong Kong Special Administrative Region of China; College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; City University of Hong Kong Shenzhen Research Institute, Shenzhen, China; State Key Laboratory of Marine Environmental Health, City University of Hong Kong, Hong Kong Special Administrative Region of China.

|July 15, 2026

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Reinforcement learning (RL) for urban drainage networks is now faster and more reliable. A new truncated and parallel rollout framework using neural network models significantly reduces training time while maintaining effective control policies.

Area of Science:

Environmental Engineering
Artificial Intelligence
Water Resource Management

Background:

Reinforcement learning (RL) shows promise for real-time control of urban drainage networks.
However, extensive simulations make RL training computationally expensive and prone to errors from surrogate models.

Purpose of the Study:

To develop an efficient and reliable RL training framework for urban drainage networks.
To address the computational cost and policy unreliability issues in current RL applications.

Main Methods:

Proposed a truncated and parallel rollout framework for RL training.
Utilized a neural network-based model (NNM) for simulations within limited horizons.
Benchmarked NNM-based RL against physics-based model (PBM)-based RL for control agent development.

Keywords:

Combined sewer overflow Neural network Real-time control Reinforcement learning Urban drainage network

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Main Results:

NNM-based RL achieved reliable training and effective control policies comparable to PBM-based RL.
Reduced agent training time by over 30-fold using a GPU-enabled parallel workflow.
Carefully selected truncated rollout horizon balanced model errors and hydraulic responses.

Conclusions:

The truncated and parallel rollout framework enhances the feasibility and cost-effectiveness of RL for real-time control of drainage networks.
This approach mitigates error accumulation and improves training efficiency without sacrificing control performance.