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](https://github.com/openai/gym/wiki/Leaderboard#lunarlandercontinuous-v2), 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 `softmax` activation for my output layer?