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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Brief Table of Contents
Chapter 1. What is reinforcement learning?
Chapter 2. Modeling reinforcement learning problems: Markov decision processes
Chapter 3. Predicting the best states and actions: Deep Q-networks
Chapter 4. Learning to pick the best policy: Policy gradient methods
Chapter 5. Tackling more complex problems with actor-critic methods
Chapter 6. Alternative optimization methods: Evolutionary algorithms
Chapter 7. Distributional DQN: Getting the full story
Chapter 8. Curiosity-driven exploration
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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.