Definition
The -return is a way to combine -step returns for different ‘s using a weighted average.
G_{t}^{\lambda} :&= (1-\lambda) \sum\limits_{n=1}^{\infty}\lambda^{n-1} G_{t:t+n}\\ &= (1-\lambda) \sum\limits_{n=1}^{T-t-1}\lambda^{n-1} G_{t:t+n} + \lambda^{T-t-1}G_{t} \end{aligned}$$ where $G_{t:t+n}$ is the [[n-Step Return]]. # Examples - $\lambda=0$: [[Temporal Difference Learning|TD]] target - $\lambda =1$: [[Monte Carlo Method|MC]] return - $0 < \lambda < 1$: creates a smooth blend of all $n$-step returns.