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 17. Beyond Model-Free – Imagination
In this chapter, we'll take a brief look at the model-based methods in Reinforcement Learning (RL) and reimplement the DeepMind model, which adds imagination to agents. Model-based methods allow us to decrease the amount of communications with the environment, by building a model of the environment and using it during the training.
Model-based versus model-free
In the Taxonomy of RL methods section in Chapter 4, The Cross-Entropy Method, we saw several different angles we can classify RL methods from. We distinguished three main aspects:
- Value-based and policy-based
- On-policy and off-policy
- Model-free and model-based
There were enough examples of methods on both sides of the first and the second categories, ...
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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.