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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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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

Summary

We started off with policy gradient methods which directly optimized the policy without requiring the Q function. We learned about policy gradients by solving a Lunar Lander game, and we looked at DDPG, which has the benefits of both policy gradients and Q functions.

Then we looked at policy optimization algorithms such as TRPO, which ensure monotonic policy improvements by enforcing a constraint on KL divergence between the old and new policy is not greater than .

We also looked at proximal policy optimization, which changed the constraint to a penalty by penalizing the large policy update. In the next chapter, Chapter 12, Capstone Project – Car Racing Using DQN, we will see how to build an agent to win a car racing game.

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