In reinforcement learning, linear function approximation is often used when large state spaces are present. (When look up tables become unfeasible.)
The form of the $Q-$value with linear function approximation is given by
$$Q(s,a) = w_1 f_1(s,a) + w_2 f_2(s,a) + \cdots, $$
where $w_i$ are the weights, and $f_i$ are the features.
The features are predefined by the user. My question is, how are the weights assigned?
I have read/downloaded some lecture slides on $Q-$learning with function approximation. Most of them have slides on linear regression that follow. Since they are just slides, they tend to be incomplete. I wonder what the connection/relation is between the two topics.