Choosing such a goodness-of-fit / quality assessment metric is always a challenge. They all have their advantages and disadvantages and it also depends a lot on what you as a modeler want to achieve with the model : Explaining the past ... forecasting the future ... accuracy ... explain-ability and interpretability ... conciseness ... .
Personally, I prefer to use AICc (or BIC) because MAPE & RMSE only look at the error without taking into account the complexity of the model. RMSE/MAPE brings therefore more risk of overfitting, while AICc/BIC uses a penalty for each additional term in the model.
An "infinite" amount has already been written about this. You're sure to find good resources. I just hope they don't just make the choice harder.