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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Adde, Nihal Acharya, Gianzina, Alexandra, Gottschalk, Hanno, Ebert, Andreas
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Optimization and Control
Online Access:	Acceder al recurso
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author	Adde, Nihal Acharya Gianzina, Alexandra Gottschalk, Hanno Ebert, Andreas
author_facet	Adde, Nihal Acharya Gianzina, Alexandra Gottschalk, Hanno Ebert, Andreas
contents	This paper introduces Evolutionary Multi-Objective Network 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. This research underscores the potential of transfer learning as a robust framework for optimizing performance across iterative learning processes by effectively leveraging knowledge from earlier generations to enhance learning efficiency and stability in subsequent generations. Experimental results demonstrate that tailored EMNAS outperforms manually designed models, achieving higher rewards with fewer parameters. The findings of these strategies contribute positively to EMNAS for RL in autonomous driving, advancing the field toward better-performing networks suitable for real-world scenarios.
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_08533
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL) Adde, Nihal Acharya Gianzina, Alexandra Gottschalk, Hanno Ebert, Andreas Machine Learning Artificial Intelligence Optimization and Control This paper introduces Evolutionary Multi-Objective Network 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. This research underscores the potential of transfer learning as a robust framework for optimizing performance across iterative learning processes by effectively leveraging knowledge from earlier generations to enhance learning efficiency and stability in subsequent generations. Experimental results demonstrate that tailored EMNAS outperforms manually designed models, achieving higher rewards with fewer parameters. The findings of these strategies contribute positively to EMNAS for RL in autonomous driving, advancing the field toward better-performing networks suitable for real-world scenarios.
title	Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)
topic	Machine Learning Artificial Intelligence Optimization and Control
url	https://arxiv.org/abs/2506.08533