April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Section 2: Building and Optimizing Q-Learning Agents
We will learn how to build more advanced Q-learning models by combining Q-learning with deep learning and giving the agent an existing model of a problem to work from. We will learn the challenges of reinforcement learning in an environment with sparse data and will work with delayed returns. The reader will become familiar with implementing deep Q-networks to solve problems and learn about the different challenges they can be used to address.
The following chapters are included in this section:
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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.