Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
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)
Other Books You May Enjoy

AlphaGo Zero

We will now continue our discussion about model-based methods by exploring the cases when we have a model of the environment, but this environment is being used by two competing parties. This situation is very familiar in board games, where the rules of the game are fixed and the full position is observable, but we have an opponent who has the primary goal of preventing us from winning the game.

Recently, DeepMind proposed a very elegant approach to solving such problems. No prior domain knowledge is required, but the agent improves its policy only via self-play. This method is called AlphaGo Zero.

In this chapter, we will:

  • Discuss the structure of the AlphaGo Zero method
  • Implement the method for playing the game Connect 4