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 16. Black-Box Optimization in RL
In this chapter, we'll again change our perspective on Reinforcement Learning (RL) training and will switch to the so-called black-box optimizations, in particular the evolution strategies and genetic algorithms. These methods are at least a decade old, but recently several research studies were conducted, which showed the applicability of the methods to large-scale RL problems and their competitiveness with the value iteration and Policy Gradient (PG) methods.
Black-box methods
In the beginning, let's discuss the whole family of methods and how they differ from what we've seen so far. Black-box optimization methods are the general approach to the optimization problem, when you treat the objective that you're ...
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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.