I come from Deep reinforcement learning. When considering simulated environments (such as AlphaZero, AlphaStar), can feature engineering dramatically improve the cpu-requirement or sample-efficiency ?

Or are low-level features the "easiest" part for the network to learn?

Edit1 : I understand of course the academic purity of working from raw data.

Edit2: so simulated means lots of samples, on policy learning, but also very cpu intensive.