Regarding the output layer's activation function for continuous action space problems I'm interested in building a (deep) RL agent for solving a continuous problem (which splits something into portions).

In all examples I've seen so far, e.g., solving the continuous lunar lander, always a $\tanh$ output layer activation was used, which produces values between $-1$ and $+1$.

Is this just because it fits the use case or is this a general rule for RL agents with continuous action spaces?

What if I just want values between $0$ and $1$? Could I simply use a $\operatorname{softmax}$ activation for my output layer?
the use of Tanh is purely because it fits the described problem (Especially for values that are min-max normalized). I have worked on couple of professional RL projects ( specifically with actions in the continuous space) and I did not use tanh at all. Hope that helped :)