# 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?