Scalable reinforcement learning-based neural architecture search
Amber Cassimon, Siegfried Mercelis, Kevin Mets
Abstract
Abstract We assess the feasibility of a reusable neural architecture search agent aimed at amortizing the initial time-investment in building a good search strategy. We do this through the use of Reinforcement Learning, where an agent learns to iteratively select the best way to modify a given neural network architecture. This is achieved using a transformer-based agent design trained using the Ape-X algorithm. We consider both the NAS-Bench-101 and NAS-Bench-301 settings, and compare against various known strong baselines, such as local search and random search. While achieving competitive performance on both benchmarks, the amount of training required for the much larger NAS-Bench-301 is only marginally greater than NAS-Bench-101, illustrating the strong scaling properties of our agent. Our agent is able to achieve strong performance, but the choice of values for certain parameters are crucial to ensuring the succesful training of the agent. We provide some guidance for the selection of appropriate values for hyperparameters through a detailed description of our experimental setup and several ablation studies.