Book Image

Reinforcement Learning with TensorFlow

By : Sayon Dutta
Book Image

Reinforcement Learning with TensorFlow

By: Sayon Dutta

Overview of this book

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
Table of Contents (21 chapters)
Title Page
Packt Upsell

Chapter 13. 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 game board at each step.

Therefore, here in this chapter we will be covering the most famous domain in...