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 15. Trust Regions – TRPO, PPO, and ACKTR
In this chapter, we'll take a look at the approaches used to improve the stability of the stochastic policy gradient method. Some attempts have been made to make the policy improvement more stable and we'll focus on three methods: Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO) and Actor-Critic (A2C) using Kronecker-Factored Trust Region (ACKTR).
To compare them to the A2C baseline, we'll use several environments from the roboschool library created by OpenAI.
Introduction
The overall motivation of the methods that we'll take a look at is to improve the stability of the policy update during the training. Intuitively, there is a dilemma: on the one hand, we'd like to train ...
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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.