  We present a simple yet efficient Hybrid Classifier based on Deep Learning
and Reinforcement Learning. Q-Learning is used with two Q-states and four
actions. Conventional techniques use feature maps extracted from Convolutional
Neural Networks (CNNs) and include them in the Qstates along with past history.
This leads to difficulties with these approaches as the number of states is
very large number due to high dimensions of the feature maps. Since our method
uses only two Q-states it is simple and has much lesser number of parameters to
optimize and also thus has a straightforward reward function. Also, the
approach uses unexplored actions for image processing vis-a-vis other
contemporary techniques. Three datasets have been used for benchmarking of the
approach. These are the MNIST Digit Image Dataset, the USPS Digit Image Dataset
and the MATLAB Digit Image Dataset. The performance of the proposed hybrid
classifier has been compared with other contemporary techniques like a
well-established Reinforcement Learning Technique, AlexNet, CNN-Nearest
Neighbor Classifier and CNNSupport Vector Machine Classifier. Our approach
outperforms these contemporary hybrid classifiers on all the three datasets
used.
