January 2020
Intermediate to advanced
432 pages
10h 18m
English
Content preview from Hands-On Reinforcement Learning for Games
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Summary
This was a short but intense chapter in which we spent time looking at various third-party DRL frameworks. Fortunately, all of these frameworks are all still free and open source, and let's hope they stay that way. We started by looking at the many growing frameworks and some pros and cons. Then, we looked at what are currently the most popular or promising libraries. Starting with Google Dopamine, which showcases RainbowDQN, we looked at how to run a quick sample of Google Colab. After that, Keras-RL was next, and we introduced ourselves to the Keras framework as well as how to use the Keras-RL library. Moving on to RLLib, we looked at the powerful automation of the DRL framework that has many capabilities. Finally, we finished up ...
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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.