June 2018
Intermediate to advanced
546 pages
13h 30m
English
book
Deep Reinforcement Learning Hands-On
by Oleg Vasilev, Maxim Lapan, Martijn van Otterlo, Mikhail Yurushkin, Basem O. F. Alijla
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Chapter 4. The Cross-Entropy Method
In this chapter, we will wrap up the part one of the book and get familiar with one of the RL methods—cross-entropy. Despite the fact that it is much less famous than other tools in the RL practitioner's toolbox, such as deep Q-network (DQN) or Advantage Actor-Critic, this method has its own strengths. The most important are as follows:
- Simplicity: The cross-entropy method is really simple, which makes it an intuitive method to follow. For example, its implementation on PyTorch is less than 100 lines of code.
- Good convergence: In simple environments that don't require complex, multistep policies to be learned and discovered and have short episodes with frequent rewards, cross-entropy usually works very well. Of ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.