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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Pages 826 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan
LinkedIn
Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
See other products by Maxim Lapan

Table of Contents (28) Chapters

Preface 1. What Is Reinforcement Learning? 2. OpenAI Gym 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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Authors (1)

Profile icon Maxim Lapan
LinkedIn
Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
See other products by Maxim Lapan

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