Is there any reason why providing symbolic features into an MLP wouldn't outperform feeding raw pixels to a CNN in a RL task? 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

Thanks
If the features are able to fully define the image, there would be no reason to use a pixel-based approach.  You are essentially performing lossless compression and (nearly) instantly acquiring a feature set, so you would see a dramatic performance difference between the CNN and MLP.

If you needed a quick solution, CNNs are great out-of-the-box algorithms, but if you are willing to put in the time to develop an MLP, it would provide a performance boost without losing accuracy.