Can i have the input to a neural network be a set of 2d coordinates if i run them through a convolution layer?

I asked this question a few days ago with no response and still dont have an answer so i will ask again. I am training a reinforcement learning agent on a 2d grid. It is fed in its position, and the target positions using x,y coordinates. An example input would be like [[1,3],[2,2],[5,1]].

I thought that since if i just fed in the input with a flatten layer (would be 1,3,2,2,5,1), there would not be a strong enough association between each coordinate pair. Because of this, i used a 1d convolution layer with 5 filters, and a step and size of 2, which i hoped would avoid the problem.

My question is if this will reach a limit; if i am using a convolution i guess i am kind of turning the input pair into one number, which seems like it cannot convey as much info as a coordinate would. Will my method work, or should i restructure my input, or maybe just go back to flattening the coordinates?