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Reinforcement Learning with TensorFlow

You're reading from   Reinforcement Learning with TensorFlow A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788835725
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Sayon Dutta Sayon Dutta
Author Profile Icon Sayon Dutta
Sayon Dutta
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Toc

Table of Contents (17) Chapters Close

Preface 1. Deep Learning – Architectures and Frameworks 2. Training Reinforcement Learning Agents Using OpenAI Gym FREE CHAPTER 3. Markov Decision Process 4. Policy Gradients 5. Q-Learning and Deep Q-Networks 6. Asynchronous Methods 7. Robo Everything – Real Strategy Gaming 8. AlphaGo – Reinforcement Learning at Its Best 9. Reinforcement Learning in Autonomous Driving 10. Financial Portfolio Management 11. Reinforcement Learning in Robotics 12. Deep Reinforcement Learning in Ad Tech 13. Reinforcement Learning in Image Processing 14. Deep Reinforcement Learning in NLP 15. Further topics in Reinforcement Learning 16. Other Books You May Enjoy

Reinforcement Learning in Image Processing

In this chapter, we will cover one of the most famous application domains in the artificial intelligence (AI) community, computer vision. Applying AI to images and videos has been going on for over two decades now. With better computational power, algorithms such as convolutional neural networks (CNNs) and its variants have worked fairly well in object detection tasks. Advanced steps have been taken towards automated image captioning, diabetic retinopathy, video object detection, captioning, and a lot more.

Due to its promising results and more generalized approach, applying reinforcement learning to computer vision successfully forms challenging tasks for researchers. We have seen how AlphaGo and AlphaGo Zero have outperformed professional human Go players, where the deep reinforcement learning approach is applied to the image of the...

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