How to deal with the limited action space in Reinforcement Learning?

Hi, I’m new in reinforcement learning and I’m recently using A3C for continuous control.

My action i.e the output of the actor network is a vector with size(64*1) and these 64 values are used as the probabilities, so all of them should be in the scale of (0,1). I use the normal distribution with the decayed variance to choose the real action and calculate the log-probabilities, but if I do so, the action will be out of range.

I tried to clip the action between 0 and 1, but that would destroy the normal distribution and lead to some bad results. Have you ever had the same problem? Any advice? Thanks!