$n$step Returns are used for value function estimation in reinforcement learning. Specifically, for $n$ steps we can write the complete return as:
$$ R_{t}^{(n)} = r_{t+1} + \gamma{r}_{t+2} + \cdots + \gamma^{n1}_{t+n} + \gamma^{n}V_{t}\left(s_{t+n}\right) $$
We can then write an $n$step backup, in the style of TD learning, as:
$$ \Delta{V}_{r}\left(s_{t}\right) = \alpha\left[R_{t}^{(n)}  V_{t}\left(s_{t}\right)\right] $$
Multistep returns often lead to faster learning with suitably tuned $n$.
Image Credit: Sutton and Barto, Reinforcement Learning
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