Open Access
ARTICLE
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: .
Journal on Artificial Intelligence 2020, 2(1), 39-58. https://doi.org/10.32604/jai.2020.09789
Received 27 January 2020; Accepted 01 April 2020; Issue published 15 July 2020
Abstract
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.
Keywords
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. https://doi.org/10.32604/jai.2020.09789
Citations