What is the difference between an RMSE and RMSLE (logarithmic error)?
Let's start with the definitions:
$$ \text{RMSE} = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} (y_i - \hat y_i)^2}, $$
$$ \text{RMSLE} = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} (\log y_i - \log \hat y_i)^2}, $$
where $y_i$ is the target value for example $i$ and $\hat y_i$ is the model's prediction. Both metrics quantify the prediction error, so in general a high RMSE implies a high RMSLE as well.

RMSLE has the meaning of a relative error, while RMSE is an absolute error. Choosing one depends on the nature of your problem. Imagine that the target spans values from around 1 to around 100. Is predicting $\hat y = 1.01$ for true $y = 1$ as bad as $\hat y = 101$ for $y = 100$? Then RMSLE might be a good choice. With RMSE, on the other hand, predicting $\hat y = 2$ for $y = 1$ is as bad as $\hat y = 101$ for $y = 100$.

In general, you would often want to use the logarithm (or maybe the square root or some other power $0 &lt; \alpha &lt; 1$) for strictly positive targets that span a large range.