Low MAE, RMSE, RMSLE and MAPE, but also a low R^2

I have a dataframe containing the IDs of 2000 questions, a list of scores representing difficulty, and the following features: how often the question was answered, how often the answer has been changed because the students were undecided, a normalized frequency of changing the answers (so the last two feature divided) and the average time spent on a question. The most important seems to be this normalized frequency (50%), then the average time (22%), how often the question was answered (17%) and how often the answer was changed overall (11%).

I used Google AutoML which is optimized for RMSE and I got:

MAE = 0.135
RMSE= 0.177
RMSLE = 0.112
MAPE  = 29.37%
R^2= 0.394

Should I worry about the R^2? How come the others look good? Is the model underfitted?