Reinforcement Learning for Adaptive Model Selection in Deep Learning

Abstract

This paper investigates the application of reinforcement learning (RL) to the problem of adaptive model selection within deep learning. Traditional model selection methods often rely on manual tuning or grid searches, which can be computationally expensive and inefficient. We propose a framework where a reinforcement agent learns to dynamically select the optimal deep learning model architecture and hyperparameters based on the observed performance. The agent's state space represents the current model configuration, and the action space comprises the available architectural choices and hyperparameter settings. The reward function is defined based on the validation performance of the selected model. Through extensive simulations, we demonstrate that our RL-based approach can effectively identify high-performing models and significantly reduce the time and resources required for model selection compared to conventional methods. The core claim is that reinforcement learning can be used to dynamically select the best deep learning model for a given task and dataset. The core mechanism involves training a reinforcement agent to explore the space of deep learning architectures and hyperparameters, learning to choose models that maximize performance.