April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Gamma – current versus future rewards
Let's discuss the concept of current rewards versus future rewards. Your agent's discount rate gamma has a value between zero and one, and its function is to discount future rewards against immediate rewards.
Your agent is deciding what action to take based not only on the reward it expects to get for taking that action, but on the future rewards it might be able to get from the state it will be in after taking that action.
One easy way to illustrate discounting rewards is with the following example of a mouse in a maze collecting cheese as rewards and avoiding cats and traps (that is, electric shocks):
The rewards that are closest to the cats, even though their point values are higher (three versus ...
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