Image Classification by Reinforcement Learning with Two-State Q-Learning
This addresses a technical bottleneck in reinforcement learning-based image classification, offering a simpler and more efficient method, though it appears incremental as it builds on existing hybrid approaches.
The paper tackles the problem of high-dimensional state spaces in reinforcement learning for image classification by proposing a hybrid classifier using two-state Q-learning, which outperforms ResNet50 and InceptionV3 on datasets like ImageNet, Cats and Dogs, and Caltech-101.
In this paper, a simple and efficient Hybrid Classifier is presented which is based on deep learning and reinforcement learning. Here, Q-Learning has been used with two states and 'two or three' actions. Other techniques found in the literature use feature map extracted from Convolutional Neural Networks and use these in the Q-states along with past history. This leads to technical difficulties in these approaches because the number of states is high due to large dimensions of the feature map. Because the proposed technique uses only two Q-states it is straightforward and consequently has much lesser number of optimization parameters, and thus also has a simple reward function. Also, the proposed technique uses novel actions for processing images as compared to other techniques found in literature. The performance of the proposed technique is compared with other recent algorithms like ResNet50, InceptionV3, etc. on popular databases including ImageNet, Cats and Dogs Dataset, and Caltech-101 Dataset. The proposed approach outperforms others techniques on all the datasets used.