Lightweight deep deterministic policy gradient for edge computing in recirculating aquaculture systems: real-time feeding control with reduced computational requirements
Wael M. Elmessery, Mahmoud Y. Shams, Tarek Abd El‐Hafeez, Péter Szűcs, Mohamed Hamdy Eid, M. Alhumedi, Atef Fathy Ahmed, Abdallah Elshawadfy Elwakeel
Abstract
The deployment of advanced reinforcement learning algorithms in edge computing environments presents significant challenges for real-time aquaculture management, particularly in resource-constrained recirculating aquaculture systems (RAS). Building upon our previous work demonstrating superior performance of DDPG controllers in commercial RAS operations, this research introduces a lightweight DDPG architecture specifically optimized for edge computing deployment in recirculating aquaculture systems. The Edge-DDPG framework reduces computational complexity by 85% while maintaining 92% of the original model's performance accuracy. The lightweight architecture employs compact neural networks with reduced layer dimensions (64→32→1 neurons vs. 400→300→1 in the original), memory-efficient replay buffers (5,000 vs. 100,000 capacity), and CPU-optimized operations suitable for ARM-based edge devices. Experimental validation demonstrates consistent performance with average inference times of 15.2 ± 3.1 ms on Raspberry Pi 4B, enabling real-time control within 50 ms system response requirements. The edge-optimized controller achieved 94.3% feeding accuracy and 96.1% water quality stability while consuming only 47 ± 8 MB of system memory. Economic analysis demonstrates deployment cost reductions from $56,900 to $8,400 for large-scale implementations, enabling widespread adoption of intelligent feeding control in small to medium-scale aquaculture operations.