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)
26
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27
Index

The Connect 4 bot

To see the method in action, let's implement AlphaGo Zero for Connect 4. The game is for two players with fields 6×7. Players have disks of two different colors, which they drop in turn into any of the seven columns. The disks fall to the bottom, stacking vertically. The game objective is to be the first to form a horizontal, vertical, or diagonal group of four disks of the same color. Two game situations are shown in the following diagram. In the first situation, the first player has just won, while in the second, the second player is going to form a group.

Figure 23.2: Two game positions in Connect 4

Despite its simplicity, this game has 4.5*1012 different game states, which is challenging for computers to solve with brute force. This example consists of several tools and library modules:

  • Chapter23/lib/game.py: A low-level game representation that contains functions to make moves, encode, and decode the game state, and other game-related...