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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Pages 826 pages
Edition 2nd Edition
Languages
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan

Table of Contents (28) Chapters

Preface What Is Reinforcement Learning? OpenAI Gym Deep Learning with PyTorch The Cross-Entropy Method Tabular Learning and the Bellman Equation Deep Q-Networks Higher-Level RL Libraries DQN Extensions Ways to Speed up RL Stocks Trading Using RL Policy Gradients – an Alternative The Actor-Critic Method Asynchronous Advantage Actor-Critic Training Chatbots with RL The TextWorld Environment Web Navigation Continuous Action Space RL in Robotics Trust Regions – PPO, TRPO, ACKTR, and SAC Black-Box Optimization in RL Advanced Exploration Beyond Model-Free – Imagination AlphaGo Zero RL in Discrete Optimization Multi-agent RL Other Books You May Enjoy
Index

Model-based methods

To begin, let's discuss the difference between the model-free approach that we have used in the book and model-based methods, including their strong and weak points and where they might be applicable.

Model-based versus model-free

In the The taxonomy of RL methods section in Chapter 4, The Cross-Entropy Method, we saw several different angles from which we can classify RL methods. 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 second categories, but all the methods that we have covered so far were 100% model-free. However, this doesn't mean that model-free methods are more important or better than their model-based counterparts. Historically, due to their sample efficiency, the model-based methods have been used in the robotics field and other industrial controls. This has also happened...

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