<ul><li>Evolutionary Multi-Objective Network Architecture Search (EMNAS) introduced for optimizing neural network architectures in large-scale Reinforcement Learning (RL) for Autonomous Driving.</li><li>EMNAS uses genetic algorithms to automate network design to enhance rewards and reduce model size without performance compromise.</li><li>Parallelization techniques and teacher-student methodologies are employed to accelerate the search and ensure scalable optimization.</li><li>Experimental results show EMNAS outperforms manually designed models, achieving higher rewards with fewer parameters, contributing to better-performing networks for real-world autonomous driving scenarios.</li></ul>

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

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