March 2020
Intermediate to advanced
384 pages
13h 10m
English
Content preview from Deep Reinforcement Learning in ActionBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 2. Modeling reinforcement learning problems: Markov decision processes
This chapter covers
- String diagrams and our teaching methods
- The PyTorch deep learning framework
- Solving n-armed bandit problems
- Balancing exploration versus exploitation
- Modeling a problem as a Markov decision process (MDP)
- Implementing a neural network to solve an advertisement selection problem
This chapter covers some of the most fundamental concepts in all of reinforcement learning, and it will be the basis for the rest of the book. But before we get into that, we want to first go over some of the recurring teaching methods we’ll employ in this book—most notably, the string diagrams we mentioned last chapter.
2.1. String diagrams and our teaching methods
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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.