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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

Gradients

Even with transparent GPU support, all of this dancing with tensors isn't worth bothering with, without one "killer feature": the automatic computation of gradients. This functionality was originally implemented in the Caffe toolkit and then became the de-facto standard in DL libraries. Computing gradients manually was extremely painful to implement and debug, even for the simplest neural network (NN). You had to calculate derivatives for all your functions, apply the chain rule, and then implement the result of the calculations, praying that everything was done right. This could be a very useful exercise for understanding the nuts and bolts of DL, but it's not something that you wanted to repeat over and over again by experimenting with different NN architectures.

Luckily, those days have gone now, much like programming your hardware using a soldering iron and vacuum tubes! Now defining an NN of hundreds of layers requires nothing more than assembling...

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