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 5. Q-Learning and Deep Q-Networks

InChapter 3Markov Decision Process, we discussed the transition model of the environment, which follows the Markov property, and the concept of delayed rewards and value (or utility) functions. Well, in this chapter we take a look at the Markov decision process, learn about Q-learning, and a modified approach called the deep Q-network for generalizing in different environments.

We will cover the following topics in this chapter:

  • Supervised and unsupervised learning for artificial intelligence
  • Model based learning and model free learning
  • Q-learning
  • Deep Q-networks
  • Monte Carlo tree search algorithm
  • SARSA algorithm