April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Epsilon – exploration versus exploitation
Your agent's exploration rate epsilon also ranges from zero to one. As the agent explores its environment, it learns that some actions are better to take than others, but what about states and actions that it hasn't seen yet? We don't want it to get stuck on a local maximum, taking the same currently highest-valued actions over and over when there might be better actions it hasn't tried to take yet.
When you set your epsilon value, there will be a probability equal to epsilon that your agent will take a random (exploratory) action, and a probability equal to 1-epsilon that it will take the current highest Q-valued action for its current state. As we step through a full Q-table update example in the ...
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