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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Length 546 pages
Edition 1st Edition
Languages
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (21) Chapters Close

Preface 1. What is Reinforcement Learning? FREE CHAPTER 2. OpenAI Gym 3. Deep Learning with PyTorch 4. The Cross-Entropy Method 5. Tabular Learning and the Bellman Equation 6. Deep Q-Networks 7. DQN Extensions 8. Stocks Trading Using RL 9. Policy Gradients – An Alternative 10. The Actor-Critic Method 11. Asynchronous Advantage Actor-Critic 12. Chatbots Training with RL 13. Web Navigation 14. Continuous Action Space 15. Trust Regions – TRPO, PPO, and ACKTR 16. Black-Box Optimization in RL 17. Beyond Model-Free – Imagination 18. AlphaGo Zero Other Books You May Enjoy Index

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...

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