April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Chapter 1, Brushing Up on Reinforcement Learning Concepts
- Reward refers to the current point value of taking an action, and value refers to the overall utility of an agent's future actions as a result of taking that action.
- A hyperparameter value is not determined by anything in the model itself and has to be set externally. Some kinds of hyperparameters might be the depth of or number of leaf nodes on a decision tree model.
- Because we don't want a learning agent to keep taking the same high-valued actions over and over if there are higher-valued actions available, an exploration strategy has it take a random action with the goal of discovering actions that might be higher-valued than the ones we've already seen. It could be taking a random ...
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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.