Why do you make the assumption that the error should increase with every step? Once your random forest is trained it basically becomes a static function outputting sometimes "good" and sometimes "bad" results. I think the only thing that can be deduced from the mape plot in your example is that it sometimes comes very close to your true result and sometimes strays away further. Do you have some kind of mathematical proof that backs your assumption?