January 2020
Intermediate to advanced
432 pages
10h 18m
English
Content preview from Hands-On Reinforcement Learning for Games
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Exercises
The exercises in this section are intended to introduce you to Unity ML-Agents in more detail. If your preference is not to use ML-Agents as a training framework, then move on to the next section and the end of this chapter. For those of you still here, ML-Agents on its own is a powerful toolkit for quickly exploring DRL agents. The toolkit hides most of the details of DRL but that should not be a problem for you to figure out by now:
- Set up and run one of the Unity ML-Agents sample environments in the editor to train an agent. This will require that you consult the Unity ML-Agents documentation.
- Tune the hyperparameters of a sample Unity environment.
- Start TensorBoard and run it so that it collects logs from the Unity runs folder. ...
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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.