In Sutton's 2020 Reinforcement Learning text (in chapter 13.7 Policy Parameterization for Continuous Actions) it's stated
actions [may be] chosen from a normal (Gaussian) distribution.
However, I can't seem to find the justification for choosing a Gaussian distribution. It seems somewhat presumptive. I understand we choose a distribution we can sample from to model a continuous action space but not why we choose a specific distribution.
Why not fit a more complicated distribution instead of just learning the mean and std. dev. of the Gaussian?
I've noticed throughout different implementations and papers, the distribution is assumed Gaussian.