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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Chapter 18. AlphaGo Zero

In the last chapter of the book, we'll continue our discussion about the model-based methods and check 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 a primary goal of preventing us from winning the game.

Recently, DeepMind proposed a very elegant approach to such problems, when no prior domain knowledge is required, but the agent improves its policy only via self-play. This method is called AlphaGo Zero, and it will be the main focus of the chapter, as we implement the method for playing the game, Connect4.