Is there a way to distill a ai model that was trained on CNN to MLP?

--------------------------------------------------------------------------- # 150-feature observation layout: # [ 0– 48] Sec 1 — 7×7 local grid 49f # [ 49– 80] Sec 2 — 8-dir whiskers ×4 32f # [ 81– 94] Sec 3 — Space & Voronoi 14f # [ 95–119] Sec 4 — 5 closest enemies (A*) 25f # [120–125] Sec 5 — Self state 6f # [126–131] Sec 6 — Open space pull + dominance 6f # [132–149] Sec 7 — Tactical signals 18f # [132–135] Cut-off opportunity per action 4f # [136–139] Corridor width per action 4f # [140–149] Enemy velocity (5 enemies × dx,dy) 10f # --------------------------------------------------------------------------- 

I am training an ai to play the game tron for a school project. I am struggling to get my ai to act the way i want (winning). i am still using a mlp policy but was considering switching to a multi input policy. i have a 150 obs space for my ai to 4 actions. most of my programing was done with the help of ai ( I am lazy). i have to port the ai to pure python which i have done for mlp before by extracting the weights to a json. The ai suggested that i distill the larger network to a smaller one. Is there a way to have a larger CNN agent teach a smaller mlp agent? if so how would i go about doing that. i can upload my code to a github if anyone want to see what i have done. edit: i forgot to mention that i am using sb3