Search icon CANCEL
Subscription
0
Cart icon
Cart
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
The Reinforcement Learning Workshop

You're reading from  The Reinforcement Learning Workshop

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781800200456
Pages 822 pages
Edition 1st Edition
Languages
Authors (9):
Alessandro Palmas Alessandro Palmas
Profile icon Alessandro Palmas
Emanuele Ghelfi Emanuele Ghelfi
Profile icon Emanuele Ghelfi
Dr. Alexandra Galina Petre Dr. Alexandra Galina Petre
Profile icon Dr. Alexandra Galina Petre
Mayur Kulkarni Mayur Kulkarni
Profile icon Mayur Kulkarni
Anand N.S. Anand N.S.
Profile icon Anand N.S.
Quan Nguyen Quan Nguyen
Profile icon Quan Nguyen
Aritra Sen Aritra Sen
Profile icon Aritra Sen
Anthony So Anthony So
Profile icon Anthony So
Saikat Basak Saikat Basak
Profile icon Saikat Basak
View More author details
Toc

Table of Contents (14) Chapters close

Preface
1. Introduction to Reinforcement Learning 2. Markov Decision Processes and Bellman Equations 3. Deep Learning in Practice with TensorFlow 2 4. Getting Started with OpenAI and TensorFlow for Reinforcement Learning 5. Dynamic Programming 6. Monte Carlo Methods 7. Temporal Difference Learning 8. The Multi-Armed Bandit Problem 9. What Is Deep Q-Learning? 10. Playing an Atari Game with Deep Recurrent Q-Networks 11. Policy-Based Methods for Reinforcement Learning 12. Evolutionary Strategies for RL Appendix

Summary

In this chapter, we learned about policy-based methods, principally the drawbacks to value-based methods such as Q-learning, which motivate the use of policy gradients. We discussed the purposes of policy-based methods of RL, along with the trade-offs of other RL approaches.

You learned about the policy gradients that help a model to learn in a real-time environment. Next, we learned how to implement the DDPG using the actor-critic model, the ReplayBuffer class, and Ornstein–Uhlenbeck noise to understand the continuous action space. We also learned how you can improve policy gradients by using techniques such as TRPO and PPO. Finally, we talked in brief about the A2C method, which is an advanced version of the actor-critic model.

Also, in this chapter, we played around with the Lunar Lander environment in OpenAI Gym—for both continuous and discrete action spaces—and coded the multiple policy-based RL approaches that we discussed.

In the next chapter...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime