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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In this chapter, we discussed the model-based approach to RL and implemented one of the recent research architectures from DeepMind, which augments the model of the environment into the model-free agents. This model tries to join both model-free and model-based paths into one, to allow the agent to decide which knowledge to use.

In the upcoming chapter (which will be the last in the book), we'll take a look at a recent DeepMind breakthrough in the area of full-information games: the AlphaGo Zero algorithm.