April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Gym environments
OpenAI Gym tasks are available as environments that provide us with a state space and an action space, along with the rewards and outcome responses that we need to train a learning agent.
There are many environments to work with; some that are simpler to use and understand than others, and new environments are being added continually. We discussed many of these different environments in detail in the last chapter:
The preceding diagram is a still from the CartPole environment, a task that we'll be solving in Chapter 6, Digging Deeper into Deep Q-Networks with Keras and TensorFlow, using a deep Q-network.
The CartPole task ...
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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.