I am tackling a RL problem (relaxed version of Space Fortress) with DQN. The usual approach would be to feed pixels into a CNN but that is usually very slow. I am considering feeding symbolic features such as coordinates, velocity and angles of the relevant elements into a Multi Layer Perceptron because then the training would be faster and I could try more things quicker. My idea is that the perceptual part of the task is not that interesting to learn here and I would like to focus on the RL part instead (I am experimenting with Hierarchical RL).
Given that with all the symbolic features that I would provide it would possible to reconstruct the full image and hence the state of the environment, would there be any reason that a CNN pixel based approach would be better?
PT: The environment has a fixed number of elements