Result of Reinforcement learning I've started reading some literature about reinforcement learning and I can't understand what is the result of the application of RL. I'll be more specific: let's have a time series problem in continuous state space, finite numbers of actions and a linear approximator of the policy function. So I follow an algorithm to find the best policy, that is, in this specific case, the optimal values of the weights of the linear function I've considered. Now my doubt is here: the so-called best policy is the one found in the process of applying the algorithm or I have to take the final optimal values and, for each period, I have to use them to find which action maximise the action-value function? In other words, the result of RL is a classic function to (re)apply at each time step, as if it was a regression? I think the answer of this question is No, but I would appreciate if someone can confirm this.

(to better explain what I meant with "policy found in the process of applying the algorithm" let's consider this stupid consideration: the best policy also include those time steps of exploration)
Is the result of RL is a classic function to (re)apply at each time step?
In some manner yes, when using RL to find the best policy you end up with a policy that can be described as a function(classic or not) from possible states to possible actions.  

As if it was a regression?
No, regression algorithms 'solves' the function between feature space and target space.
 In RL both of these spaces have no(very different) meanings.
In addition, RL algorithms take into consideration multi-step predictions(state transitions + rewards), which is not very straight-forward in regression problems.