Robust Evolutionary Multi-Objective Neural Architecture Search for Reinforcement Learning (EMNAS-RL)

This paper introduces Evolutionary Multi-Objective Neural Architecture Search (EMNAS) for the first time to optimize neural network architectures in large-scale Reinforcement Learning (RL) for Autonomous Driving (AD).EMNAS uses genetic algorithms to automate network design, tailored to enhance rewards and reduce model size without compromising performance.Additionally, parallelization techniques are employed to accelerate the search, and teacher-student methodologies are implemented to ensure scalable optimization.Experimental results demonstrate that tailored EMNAS outperforms manually designed models, achieving higher rewards with fewer parameters.