# What is the intuition behind TD($\lambda$)?

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?

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.