Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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Connect4 bot

To see the method in action, let's implement AlphaGo Zero for Connect4. The game is for two players with fields 6 × 7. Players have disks of two different colors, which they drop in turn to any of the seven columns. 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 diagram. On the first, the red player has just won, while on the second, the blue player is going to form a group.

Figure 2: Two game positions in Connect4

Despite the 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:

  • Chapter18/lib/ Low-level game representation, which contains functions to make moves, encode and decode the game state, and other game-related utilities.

  • Chapter18/lib/ MCTS implementation that allows GPU-expansion...