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Why changed the epsilon in Q-learning and the way to update Q, is this better? #34

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baimengwei opened this issue May 29, 2020 · 0 comments

baimengwei commented May 29, 2020

The epsilon obtained is 1-1 / (log (n + 2)), and the update Q uses 1 / sqrt (n + 2)。
Seems like a good choice, is there any basis for doing so?

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