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


In this chapter, we understood the basic concepts and challenges in the domain of advertising technology. We also learned about the relevant business models, such as CPC, CPM, and CPA, and real-time strategy bidding and why there's a need for an autonomous agent to automate the process. Moreover, we discussed a basic approach to converting the problem state of real-time bidding in online advertising into a reinforcement-learning framework. This is a totally new domain for reinforcement learning to disrupt. Many more exploratory works utilizing reinforcement learning for advertising technology, and their results, are yet to be published.

In the next chapter, we will study how reinforcement learning is being used in the field of computer vision, especially for object detection.