# How best to dynamically update online decision trees weighting?

I have a series of 3-4 attributes (call them $X_1$, $X_2$, $X_3$) which can all have values of, say, any number on $[1,100]$. What I'd like to do is, first, build a group of decision trees (call them $T_1, T_2,..., T_n$) and weight them using a training set. Then, depending on how well each tree is doing at a given time on a given day, as data is fed in (thereby changing the attribute values), I'd like to use some form of online reinforcement learning to dynamically adjust the weights of each tree. Is this possible? I come from a non-Statistics background so I'm not familiar with the current literature in an area like this. Any help would be great!