September 2018
Intermediate to advanced
296 pages
9h 10m
English
Content preview from Python Reinforcement Learning Projects
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What this book covers
Chapter 1, Up and Running with Reinforcement Learning, introduces AI, RL, deep learning, the history/applications of the field, and other relevant topics. It will also provide a high-level overview of fundamental deep learning and TensorFlow concepts, especially those relevant to RL.
Chapter 2, Balancing Cart Pole, will have you implement your first RL algorithms in Python and TensorFlow to solve the cart pole balancing problem.
Chapter 3, Playing Atari Games, will get you creating your first deep RL algorithm to play ATARI games.
Chapter 4, Simulating Control Tasks, provides a brief introduction to actor-critic algorithms for continuous control problems. You will learn how to simulate classic control tasks, look at ...
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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.