You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Length 826 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Chatbots

Author (1):

Maxim Lapan

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Table of Contents (28) Chapters

Preface

1. What Is Reinforcement Learning?

2. OpenAI Gym FREE CHAPTER

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. Higher-Level RL Libraries

8. DQN Extensions

9. Ways to Speed up RL

10. Stocks Trading Using RL

11. Policy Gradients – an Alternative

12. The Actor-Critic Method

13. Asynchronous Advantage Actor-Critic

14. Training Chatbots with RL

15. The TextWorld Environment

16. Web Navigation

17. Continuous Action Space

18. RL in Robotics

19. Trust Regions – PPO, TRPO, ACKTR, and SAC

20. Black-Box Optimization in RL

21. Advanced Exploration

22. Beyond Model-Free – Imagination

23. AlphaGo Zero

24. RL in Discrete Optimization

25. Multi-agent RL

26. Other Books You May Enjoy

27. Index

Other RL libraries

As we discussed earlier, there are several RL-specific libraries available. Overall, TensorFlow is more popular than PyTorch, as it is more widespread in the deep learning community. The following is my (very biased) list of libraries:

Keras-RL: started by Matthias Plappert in 2016, this includes basic deep RL methods. As suggested by the name, this library was implemented using Keras, which is a higher-level wrapper around TensorFlow (https://github.com/keras-rl/keras-rl).
Dopamine: a library from Google published in 2018. It is TensorFlow-specific, which is not surprising for a library from Google (https://github.com/google/dopamine).
Ray: a library for distributed execution of machine learning code. It includes RL utilities as part of the library (https://github.com/ray-project/ray).
TF-Agents: another library from Google published in 2018 (https://github.com/tensorflow/agents).
ReAgent: a library from Facebook Research. It uses PyTorch...

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You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Table of Contents (28) Chapters

Other RL libraries

Authors (1)

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You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Table of Contents (28) Chapters

Other RL libraries

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Authors (1)

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