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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Model-based versus model-free

In the Taxonomy of RL methods section in Chapter 4, The Cross-Entropy Method, we saw several different angles we can classify RL methods from. We distinguished three main aspects:

  • Value-based and policy-based

  • On-policy and off-policy

  • Model-free and model-based

There were enough examples of methods on both sides of the first and the second categories, but all the methods we've seen so far were 100% model-free. This doesn't mean that model-free methods are more important or better than their model-based antagonists. Historically, due to their sample-efficiency, the model-based methods have been used in the robotics field and other industrial controls. That is happened due to the cost of the hardware and the physical limitations of samples that could be obtained from a real robot. Robots with a large amount of degrees of freedom are not widely accessible, so RL researchers are more focused on computer games and other environments where samples are relatively cheap...