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

Controlling the hardware

In this section, I will describe how we can use the trained model on the real hardware.

MicroPython

For a very long time, the only option in embedded software development was using low-level languages like C or assembly. There are good reasons behind this: limited hardware capabilities, power-efficiency constraints, and the necessity of dealing with real-world events predictably. Using a low-level language, you normally have full control over the program execution and can optimize every tiny detail of your algorithm, which is great.

The downside of this is complexity in the development process, which becomes tricky, error-prone, and lengthy. Even for hobbyist projects that don't have very high efficiency standards, platforms like Arduino offer a quite limited set of languages, which normally includes C and C++.

MicroPython (http://micropython.org) provides an alternative to this low-level development by bringing the Python interpreter to microcontrollers...