RL based hyper-parameters optimization algorithm (ROA) for convolutional neural network

Abstract

Abstract Many real-world applications necessitate optimization in dynamic situations, where the difficulty is to locate and follow the optima of a time-dependent objective function. To solve dynamic optimization problems (DOPs), many evolutionary techniques have been created. However, more efficient solutions are still required. Recently, a new intriguing trend in dealing with optimization in dynamic environments has developed, with new reinforcement learning (RL) algorithms predicted to breathe fresh life into the DOPs community. In this paper, a new Q-learning RL-based optimization algorithm (ROA) for CNN hyperparameter optimization is proposed. Two datasets were used to test the proposed RL model (MNIST dataset, and CIFAR-10 dataset). Due to the use of RL for hyperparameter optimization, very competitive results and good performance were produced. From the experimental results, it is observed that the CNN optimized by ROA has higher accuracy than CNN without optimization. When using the MNIST dataset, it is shown that the accuracy of the CNN optimized by ROA when learning 5 epoch is 98.97%, which is greater than the 97.62% of the CNN without optimization. When using the CIFAR-10 dataset, it is shown that the accuracy of the CNN optimized by ROA when learning 10 epoch is 73.40 percent, which is greater than 71.73% of the CNN without optimization.