Automating Multi-element Subspace Exploration via Reinforcement Learning

Machine learning enters many aspects of our lives and brings us great convenience. However, building an effective machine learning model for a specific task requires not only expertise but also a lot of time and resources. In order to solve this problem, more and more research projects focus on automated machine learning (AutoML). In this paper, we propose an algorithm that can simultaneously optimize the space of multiple datasets, multiple models, and multiple hyperparameters. We call this an automating multi-element subspace exploration algorithm. We first formalize this problem as a reinforcement learning problem and then we define the state, action and well-designed reward function in reinforcement learning system. In addition, we use some skills and experience to accelerate the entire optimization process. Finally, our experimental results on multiple tasks demonstrate that our method is effective.