I want to use Reinforcement Learning to optimize the distribution of energy for a peak shaving problem given by a thermodynamical simulation. However, I am not sure how to proceed as the action space is the only thing that really matters, in this sense:
- The action space is a 288x66 matrix of real numbers between 0 and 1. The output of the simulation and therefore my reward depend solely on the distribution of this matrix.
- The state space is therefore absent, as the only thing that matters is the matrix on which I have total control. At this stage of the simulation, no other variables are taken into consideration.
I am not sure if this problem falls into the tabular RL or it requires approximation. In this case, I was thinking about using a Policy gradient algorithm for figuring out the best distribution of the 288x66 matrix; however, I do not know how to behave with the "absence" of the state space. Instead of a tuple I would just have , is this even a RL-approachable problem? If not, how can I reshape it to make it solvable with RL techniques?