The approach as described has one major downside. It is based on presumption that human players will employ the same strategies as a neural network which learne

The approach as described has one major downside. It is based on presumption that human players will employ the same strategies as a neural network which learned to play the game. It may be the case, but in reality many imbalances in the game model remain undiscovered or unused by the real players, for various reasons. The good example of ML for playtesting is what King is doing with their Candy Crash Saga. They have trained neural network on real world usage data, from millions of players. That makes it behave like a real player too, not pathetically weak, and not inhumanely strong. This, if applicable for your game, is a better way to leverage ML. There are other examples of neural networks finding highly unorthodox strategies in various games, when they learning to play it. It is nothing like human behaviour.