Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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Index

Tabular Learning and the Bellman Equation

In the previous chapter, you became acquainted with your first reinforcement learning (RL) algorithm, the cross-entropy method, along with its strengths and weaknesses. In this new part of the book, we will look at another group of methods that has much more flexibility and power: Q-learning. This chapter will establish the required background shared by those methods.

We will also revisit the FrozenLake environment and explore how new concepts fit with this environment and help us to address issues of its uncertainty.

In this chapter, we will:

  • Review the value of the state and value of the action, and learn how to calculate them in simple cases
  • Talk about the Bellman equation and how it establishes the optimal policy if we know the values
  • Discuss the value iteration method and try it on the FrozenLake environment
  • Do the same for the Q-learning method

Despite the simplicity of the environments in this chapter...