Yes and no, essentially the decision trees will give you something quite similar (essentially just a bunch of boolean weighted sums), the problem with this is that, for an effective model, you'll have thousands of these, each with different weights for each statement, then another weight for each model (since RF averages over various models). So, it is theoretically possible, just not straightforward to interpret, visualize, or explain. You could consider an example with n features, and 2 regression trees with a shrinkage rate of 0.5. This would be reasonably straightforward to interpret, but once you have 500 trees interpretation is much less trivial.