I am trying to design reinforcement learning algorithm. My action and state space are continuous. Action, which I would like to take can be represented by a matrix, lets say of dimension $n \times n$. And I would like to use reinforcement learning to find optimal entries of this matrix.
So far, I looked into deep reinforce algorithm. However, it is not clear to me how to design network/algorithm, such that it allows for modifying entries of the matrix.
As far as I understood, output of the network should represent probability, with which we take certain action. I wonder, do I need to model each dimension of the action(each element of the matrix) as an independent prob. process? Is it good idea to model it by the same network?