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

Telegram bot

As a final step, the Telegram chatbot using the trained model was implemented. To be able to run it, you need to install the python-telegram-bot extra package into your virtual environment using pip install.

Another step you need to take to start the bot is to obtain the API token by registering the new bot. The complete process is described in the documentation, https://core.telegram.org/bots#6-botfather. The resulting token is a string of the form 110201543:AAHdqTcvCH1vGWJxfSeofSAs0K5PALDsaw.

The bot requires this string to be placed in a configuration file in ~/.config/rl_Chapter14_bot.ini, and the structure of this file is shown in the Telegram bot source code as follows. The logic of the bot is not very different from the other two tools used to experiment with the model: it receives the phrase from the user and replies with the sequence generated by the decoder.

#!/usr/bin/env python3
# This module requires python-telegram-bot
import os
import sys
import...

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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

Telegram bot

Authors (1)

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Table of Contents (28) Chapters

Telegram bot

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