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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Even with transparent GPU support, all of this dancing with tensors isn't worth bothering with, without one "killer feature": the automatic computation of gradients. This functionality was originally implemented in the Caffe toolkit and then became the de-facto standard in DL libraries. Computing gradients manually was extremely painful to implement and debug, even for the simplest neural network (NN). You had to calculate derivatives for all your functions, apply the chain rule, and then implement the result of the calculations, praying that everything was done right. This could be a very useful exercise for understanding the nuts and bolts of DL, but it's not something that you wanted to repeat over and over again by experimenting with different NN architectures.

Luckily, those days have gone now, much like programming your hardware using a soldering iron and vacuum tubes! Now defining an NN of hundreds of layers requires nothing more than assembling it from predefined building...