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

Roboschool

To experiment with the methods in this chapter, we will use Roboschool, which uses PyBullet as a physics engine and has 13 environments of various complexity. PyBullet has similar environments, but at the time of writing, it isn't possible to create several instances of the same environment due to an internal OpenGL issue.

In this chapter, we will explore two problems: RoboschoolHalfCheetah-v1, which models a two-legged creature, and RoboschoolAnt-v1, which has four legs. Their state and action spaces are very similar to the Minitaur environment that we saw in Chapter 17, Continuous Action Space: the state includes characteristics from joints, and the actions are activations of those joints. The goal for both is to move as far as possible, minimizing the energy spent. Figure 19.1 shows screenshots of the two environments.