Survey on Object Detection Using Deep Reinforcement Learning

Abstract

Deep Reinforcement Learning (DRL) is a method that is a combination of Reinforcement Learning framework and deep neural networks. It is observed that DRL achieved a remarkable victory over the fields such as video games, robotics, finance, computer vision, health care etc. Comparing other domains, the medicine and healthcare field has benefitted a lot from DRL. In this paper, we study the role of DRL in object detection using the works of various authors. Here we focus on object detection in medicine and the healthcare field. It is observed that the authors experience higher speed in the DRL algorithm compared to classic methods. The respective methods are more efficient and accurate working on CT/MRI images. Most authors use an updated DRL algorithm in the stage of feature extraction and also club it with some machine learning techniques. DQN (Deep Q Network), Double DQN, TRPO(Trust Region Policy Optimization) etc are some common DRL algorithms used by researchers. This literature survey emphasizes methodologies of application of DRL algorithms for more efficient object detection. This review helps the futuristic way to develop a DRL algorithm for better object detection in the healthcare domain and similar ones.