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Why are these two implementations of the $\epsilon$-greedy policy different?
According to the book Reinforcement Learning An Introduction, the epsilon greedy policy can generally implemented as: $$ \pi(a|s) = \begin{cases} \frac{\epsilon}{|A|} + 1 - \epsilon & \text{if } ...
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