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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In this chapter, we saw a quick overview of PyTorch functionality and features. We talked about basic fundamental pieces such as tensor and gradients, saw how an NN can be made from the basic building blocks, and learned how to implement those blocks ourselves. We discussed loss functions and optimizers, as well as the monitoring of training dynamics. The goal of the chapter was to give a very quick introduction to PyTorch, which will be used later in the book.

For the next chapter, we're ready to start dealing with the main subject of this book: RL methods.