Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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Hardware and software requirements

The examples in this book were implemented and tested using Python version 3.6. I assume that you're already familiar with the language and common concepts such as virtual environments, so I won't cover in detail how to install the package and how to do this in an isolated way. The external libraries we'll use in this book are open source software, including the following:

  • NumPy: This is a library for scientific computing and implementing matrix operations and common functions.

  • OpenCV Python bindings: This is a computer vision library, which provides many functions for image processing.

  • Gym: This is a RL framework developed and maintained by OpenAI with various environments that can be communicated with, in a unified way.

  • PyTorch: This is a flexible and expressive Deep Learning (DL) library. A short essential crash course on it will be given in the next chapter.

  • Ptan This is an open source extension to Gym created by the...