L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning

• URL: http://arxiv.org/abs/2109.12425v1
• Date: Sat, 25 Sep 2021 19:26:30 GMT
• Title: L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
• Authors: Keith G. Mills, Fred X. Han, Mohammad Salameh, Seyed Saeed Changiz Rezaei, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui and Di Niu
• Abstract summary: Differentiable architecture search (NAS) achieved remarkable results in deep neural network design. We show that L$2$ achieves state-of-theart results on DART201 benchmark as well as NASS and Once-for-All search policies.
• Score: 23.25155249879658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures into a hyperparameter optimization problem which can be solved by gradient descent. However, questions have been raised regarding the effectiveness and generalizability of gradient methods for solving non-convex architecture hyperparameter optimization problems. In this paper, we propose L$^{2}$NAS, which learns to intelligently optimize and update architecture hyperparameters via an actor neural network based on the distribution of high-performing architectures in the search history. We introduce a quantile-driven training procedure which efficiently trains L$^{2}$NAS in an actor-critic framework via continuous-action reinforcement learning. Experiments show that L$^{2}$NAS achieves state-of-the-art results on NAS-Bench-201 benchmark as well as DARTS search space and Once-for-All MobileNetV3 search space. We also show that search policies generated by L$^{2}$NAS are generalizable and transferable across different training datasets with minimal fine-tuning.

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