April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Questions
- Why do we choose to use the words state and observation interchangeably? When would be a more appropriate time to use the word state?
- How do we know when the Q-function has converged?
- What happens to the Q-table when the Q-function has converged?
- When do we know the agent has found the optimal path to the goal? Describe in terms of the previous two questions.
- What does numpy.argmax() return?
- What does numpy.max() return?
- Why does the randomly-acting agent take thousands of time steps to reach the goal? How does the Q-learning agent perform better?
- Describe one benefit of decaying alpha.
- What is overfitting and how does it apply in the context of an RL model?
- By what order of magnitude does the number of time steps needed to ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.