Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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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, 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