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
Contributors
Preface
Index

Summary


In this chapter, we studied the best reinforcement learning architecture at the moment, that is AlphaGo. We understood the reason behind choosing Go and its complexity with respect to chess. We also learnt how DeepBlue AI architecture works and how a different and better architecture and training process is needed for Go. We studied the architectures and training processes used in AlphaGo and AlphaGo Zero, and also understood the differences between the versions and how AlphaGo Zero surpassed its earlier versions. 

In the next chapter, we will study how reinforcement learning can be used and implemented in autonomous and self-driving cars.