Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Reinforcement Learning with Python

You're reading from   Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Arrow left icon
Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788836524
Length 318 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Introduction to Reinforcement Learning 2. Getting Started with OpenAI and TensorFlow FREE CHAPTER 3. The Markov Decision Process and Dynamic Programming 4. Gaming with Monte Carlo Methods 5. Temporal Difference Learning 6. Multi-Armed Bandit Problem 7. Deep Learning Fundamentals 8. Atari Games with Deep Q Network 9. Playing Doom with a Deep Recurrent Q Network 10. The Asynchronous Advantage Actor Critic Network 11. Policy Gradients and Optimization 12. Capstone Project – Car Racing Using DQN 13. Recent Advancements and Next Steps 14. Assessments 15. Other Books You May Enjoy

Prioritized experience replay

In DQN architecture, we use experience replay to remove correlations between the training samples. However, uniformly sampling transitions from the replay memory is not an optimal method. Instead, we can prioritize transitions and sample according to priority. Prioritizing transitions helps the network to learn swiftly and effectively. How do we prioritize the transitions? We prioritize the transitions that have a high TD error. We know that a TD error specifies the difference between the estimated Q value and the actual Q value. So, transitions with a high TD error are the transition we have to focus on and learn from because those are the transitions that deviate from our estimation. Intuitively, let us say you try to solve a set of problems, but you fail in solving two of these problems. You then give priority to those two problems alone to focus...

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 €18.99/month. Cancel anytime