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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Deterministic policy gradients
The next method that we'll take a look at is called deterministic policy gradients, which is a variation of the A2C method, but has a very nice property of being off-policy. The following is my very relaxed interpretation of the strict proofs. If you are interested in understanding the core of this method deeply, you may always refer to the article by David Silver and others called Deterministic Policy Gradient Algorithms, published in 2014 and the paper by Timothy P. Lillicrap and others called Continuous Control with Deep Reinforcement Learning, published in 2015.
The simplest way to illustrate the method is by comparison with the already familiar A2C. In this method, the actor estimates the stochastic policy, ...
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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.