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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Distributional policy gradients
As the last method of this chapter, we'll take a look at the very recent paper by Gabriel Barth-Maron, Matthew W. Hoffman, and others, called Distributional Policy Gradients, published in 2018. At the time of writing, this paper hasn't been uploaded to ArXiV yet, as it was only submitted for a review for the conference ICLR 2018. It is available at https://openreview.net/forum?id=SyZipzbCb.
The full name of the method is Distributed Distributional Deep Deterministic Policy Gradients or D4PG for short. The authors proposed several improvements to the DDPG method we've just seen to improve stability, convergence, and sample efficiency.
First of all, they adapted the distributional representation of the Q-value proposed ...
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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.