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Survey on the Application of Deep Reinforcement Learning in Image Processing

Wei Fang1, 2, 3, ∗, Lin Pang1, Weinan Yi1

1 School of Computer & Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China.
2 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
3 Jiangsu Key Laboratory of Computer Information Process Technology, Soochow University, Suzhou, China.

* Corresponding Author: Wei Fang. Email: email.

Journal on Artificial Intelligence 2020, 2(1), 39-58.


In recent years, with the rapid development of human society, more and more complex tasks have emerged that require deep learning to automatically extract abstract feature representations from a large amount of data, and use reinforcement learning to learn the best strategy to complete the task. Through the combination of deep learning and reinforcement learning, end-to-end input and output can be achieved, and substantial breakthroughs have been made in many planning and decision-making systems with infinite states, such as games, in particular, AlphaGo, robotics, natural language processing, dialogue systems, machine translation, and computer vision. In this paper we have summarized the main techniques of deep reinforcement learning and its applications in image processing.


Cite This Article

W. Fang, L. Pang and W. Yi, "Survey on the application of deep reinforcement learning in image processing," Journal on Artificial Intelligence, vol. 2, no.1, pp. 39–58, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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