April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Gamma
In this task, we choose a value of 1 for gamma. Recall that gamma determines how much the agent prioritizes current over future rewards. One purpose of gamma is to penalize the agent for taking too long to find a solution.
In the CartPole problem, the agent's goal is to remain active for as long as possible, since in each state the choice is between moving the wrong way and making the pole fall over or moving the right way and keeping it upright.
The only thing that matters to the agent in this task is the next state. For this reason, we want the agent to prioritize staying alive over everything else and not to take future rewards into account at all.
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