I have a question about a reinforcement learning problem.
I'm training an agent to add or delete pixels in a [12 x 12] 2D space (going to be 3D in the future). Its action space consists of two discrete outputs: x[0-12] and y[0-12].
What would be the value of instead outputting a (continuous) probabilistic output representation, like the [12 x 12] space with each pixel as a probability, and sampling from it. E.g. a softmax function applied to 144 (12*12) output nodes.
My environment is deterministic itself: taking action 𝑎 in state 𝑠 always results in the same next state 𝑠′.
I understand that this may be more difficult to train since the output space becomes continuous instead of discrete, and therefore bigger, but does stochastic/probabilistic output have any benefits over 1 discrete output?