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The Weak Law of Large Numbers (WLLN) says that the sample average of independent, identically distributed (i.i.d.) random variables with finite mean gets close to the true mean with high probability as the sample size grows.
The discounted return G_t sums all future rewards but down-weights distant rewards by powers of a discount factor ฮณ.