Abstract
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.
Abstract (translated)
本文首次介绍了进化多目标网络架构搜索（EMNAS），用于优化大规模强化学习（RL）中自动驾驶（AD）的神经网络架构。EMNAS利用遗传算法自动化网络设计，旨在提升奖励并减小模型规模而不影响性能。此外，还采用了并行化技术以加速搜索过程，并实施了师生方法确保可扩展性优化。这项研究强调了迁移学习作为迭代学习过程中优化性能的强大框架潜力，通过有效利用早期代的知识来提高后续代的学习效率和稳定性。实验结果表明，定制的EMNAS优于手动设计的模型，在参数更少的情况下获得更高的奖励。这些策略的研究成果对增强EMNAS在自动驾驶强化学习中的应用贡献积极，并推动该领域向更适合现实场景的高性能网络发展。
URL
https://arxiv.org/abs/2506.08533
