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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Trust Region Policy Optimization
TRPO was proposed in 2015 by the Berkeley researchers in the paper by John Schulman et al called Trust Region Policy Optimization (arXiv:1502.05477). This paper was a step towards improving the stability and consistency of the stochastic policy gradient optimization and has shown good results on various control tasks.
Unfortunately, the paper and the method are quite math-heavy, so it can be hard to understand the details of the method. The same could be said about the implementation, which uses the conjugate gradients method to efficiently solve the constrained optimization problem.
As the first step, the TRPO method defines the discounted visitation frequencies of the state: . In this equation, equals to the sampled ...
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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.