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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

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788836524
Length 318 pages
Edition 1st Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Toc

Table of Contents (16) Chapters Close

Preface 1. Introduction to Reinforcement Learning FREE CHAPTER 2. Getting Started with OpenAI and TensorFlow 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

Hindsight experience replay

We have seen how experience replay is used in DQN to avoid a correlated experience. Also, we learned that prioritized experience replay is an improvement to the vanilla experience replay as it prioritizes each experience with the TD error. Now we will look at a new technique called hindsight experience replay (HER), proposed by OpenAI researchers for dealing with sparse rewards. Do you remember how you learned to ride a bike? On your first try, you wouldn't have balanced the bike properly. You would have failed several times to balance correctly. But all those failures don't mean you didn't learn anything. The failures would have taught you how not to balance a bike. Even though you did not learn to ride the bike (goal), you learned a different goal, that is, you learned how not to balance a bike. This is how we humans learn, right? We...

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