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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I2A on Atari Breakout
The code and training path of I2A is a bit complicated and includes lots of code and several steps. To understand it better, let's start with a brief overview. In this example, we'll implement the I2A architecture described in the paper, adopted to the Atari environments, and test it on the Breakout game. The overall goal is to check the training dynamics and the effect of imagination augmentation on the final policy.
Our example consists of three parts, which correspond to different steps in the training:
- Baseline A2C agent in
Chapter17/01_a2c.py. The resulting policy is used for obtaining observations of the environment model. - Environment model training in
Chapter17/02_imag.py. It uses the model obtained on the previous step ...
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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.