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 touched on the main concepts and challenges related to one of the biggest AI problems, that is, autonomous driving. We learned about the challenges posed by the problem and also learned the current approaches being used to make autonomous driving successful. Moreover, we went through an overview of different sub-tasks of the process, starting from receiving sensory inputs to planning. We also looked at a bit about the famous DeepTraffic simulation where you can test your neural networks to learn efficient movement patterns in heavy traffic. Autonomous driving is itself a vast evolving research topic and covering all of them is beyond the scope of this book. 

In the next chapter, we will study another evolving research hotspot, using AI in finance, where we will learn how reinforcement can help in financial portfolio management.