Harnessing deep reinforcement learning algorithms for image categorization: A multi algorithm approach

Abstract

Image categorization is an important task in the field of Artificial Intelligence. Deep reinforcement learning (DRL) algorithms are effective for image categorization, especially in the real-time scenarios. In this study, we explore how different DRL algorithms can be used for classifying images in the field of artificial intelligence across multiple benchmark datasets. Our primary goal is to illustrate the way DRL algorithms perform comparable to conventional machine learning(ML) and deep learning(DL) methods. This usage of diverse data allows to evaluate thoroughly how this DRL algorithms will adapt and learn under different situations similar to real-world scenarios with unpredictable data. Our findings suggest that deep reinforcement learning algorithms outperform other algorithms in environments with diverse and complex input. The DRL methods achieved greater accuracies when compared with most of the machine learning and deep learning approaches at 1 Million timesteps. Among the DRL models, Recurrent Proximal Policy Optimization(RPPO) with an accuracy of 97.57% for MNIST dataset, 89% for KMNIST dataset, 89% for EMNIST dataset and Deep Q Network(DQN) on Fashion_MNIST dataset with an accuracy of 87.40% outperformed most of the deep learning and machine learning models and some achieving almost similar to the deep learning and machine learning models. This study not only demonstrates DRL’s capability to handle real-time challenges but also underscores its importance as a valuable tool for computer vision tasks, such as image categorization.