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 7. Distributional DQN: Getting the full story
This chapter covers
- Why a full probability distribution is better than a single number
- Extending ordinary deep Q-networks to output full probability distributions over Q values
- Implementing a distributional variant of DQN to play Atari Freeway
- Understanding the ordinary Bellman equation and its distributional variant
- Prioritizing experience replay to improve training speed
We introduced Q-learning in chapter 3 as a way to determine the value of taking each possible action in a given state; the values were called action values or Q values. This allowed us to apply a policy to these action values and to choose actions associated with the highest action values. In this chapter we will extend ...
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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.