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

The A2C baseline

To establish the baseline results, we will use the A2C method in a very similar way to the code in the previous chapter.

Implementation

The complete source is in files Chapter19/01_train_a2c.py and Chapter19/lib/model.py. There are a few differences between this baseline and the version we used in the previous chapter. First of all, there are 16 parallel environments used to gather experience during the training. The second difference is the model structure and the way that we perform exploration. To illustrate them, let's look at the model and the agent classes.

Both the actor and critic are placed in the separate networks without sharing weights. They follow the approach used in the previous chapter, with our critic estimating the mean and the variance for the actions. However, now variance is not a separate head of the base network; it is just a single parameter of the model. This parameter will be adjusted during the training by SGD, but it doesn...