April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Bellman equations
As we mentioned, the Q-table functions as your agent's brain. Everything it has learned about its environment is stored in this table. The function that powers your agent's decisions is called a Bellman equation. There are many different Bellman equations, and we will be using a version of the following equation:
Here, newQ(s,a) is the new value that we are computing for the state-action pair to enter into the Q-table; Q(s,a) is the current state; alpha is the learning rate; R(s,a) is the reward for that state-action pair; gamma is the discount rate; and maxQ(s', a') is the maximum expected future reward given to the new ...
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