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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Summary
In this chapter, we walked through and implemented lots of DQN improvements discovered by researchers since the first DQN paper was published in 2015. This list is far from complete. First of all, for the list of methods, I've used the paper, [1] Rainbow: Combining Improvements in Deep Reinforcement Learning, which was published by DeepMind, so the list of methods is definitely biased to DeepMind papers. Secondly, RL is so active nowadays that new papers come out almost every day, which makes it very hard to keep up with, even if we limit ourselves to one kind of RL model such as a DQN. The goal of this chapter was to give you a practical view of different ideas that the field has developed.
In the next chapter, we will apply our DQN knowledge ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.