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

View More author details

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

Monitoring with TensorBoard

If you have ever tried to train an NN on your own, then you will know how painful and uncertain it can be. I'm not talking about following the existing tutorials and demos, when all the hyperparameters are already tuned for you, but about taking some data and creating something from scratch. Even with modern DL high-level toolkits, where all best practices, such as proper weights initialization; optimizers' betas, gammas, and other options set to sane defaults; and tons of other stuff hidden under the hood, there are still lots of decisions that you can make, hence lots of things that could go wrong. As a result, your network almost never works from the first run and this is something that you should get used to.

Of course, with practice and experience, you will develop a strong intuition about the possible causes of problems, but intuition needs input data about what's going on inside your network. So, you need to be able to peek inside...