# What are finite horizon look-ahead policies in reinforcement learning?

I was reading the paper How to Combine Tree-Search Methods in Reinforcement Learning published in AAAI Conference 2019. It starts with the sentence

Finite-horizon lookahead policies are abundantly used in Reinforcement Learning and demonstrate impressive empirical success.

What is meant by "finite horizon look-ahead"?

Per this paper a look ahead policy is a policy that will make decisions based on some 'horizon'. Here horizon means some time steps into the future, and so a finite horizon is simply a finite amount of time steps into the future. For example, as we are typically concerned with maximising returns in RL, a 10-step look ahead policy would choose an action at time $$t$$ that maximises the (expected) rewards at time $$t+1, ... t+10$$.