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I'd like to better understand temporal-difference learning. In particular, I'm wondering if it is prudent to think about TD($\lambda$) as a type of "truncated" Monte Carlo learning?

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TD($\lambda$) can be thought of as a combination of TD and MC learning, so as to avoid to choose one method or the other and to take advantage of both approaches.

More precisely, TD($\lambda$) is temporal-difference learning with a $\lambda$-return, which is defined as an average of all $n$-step returns, for all $n$, where an $n$-step return is the target used to update the estimate of the value function that contains $n$ future rewards (plus an estimate of the value function of the state $n$ steps in the future). For example, TD(0) (e.g. Q-learning is usually presented as a TD(0) method) uses a $1$-step return, that is, it uses one future reward (plus an estimate of the value of the next state) to compute the target. The letter $\lambda$ actually refers to a parameter used in this context to weigh the combination of TD and MC methods. There are actually two different perspectives of TD($\lambda$), the forward view and the backward view (eligibility traces).

The blog post Reinforcement Learning: Eligibility Traces and TD(lambda) gives a quite intuitive overview of TD($\lambda$), and, for more details, read the related chapter of the book Reinforcement Learning: An Introduction.

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