What's the loss function for this? How do you cross-validate this loss? The only generally applicable methods I can think of (essentially some Bayesian-type updating, which then uses Monte-Carlo-type methods for evaluating this loss and "uncertainty") is just so much more expensive than anything we do at the moment that, at the current computing level, we wouldn't even be able to do it in most cases.
The problem is, an AI model will tell you an "uncertainty" but these "uncertainties" will often not reflect the true underlying probability that it is wrong. This is, of course, all a bit silly since it's hard knowing what you don't know, and a proper update to this number is also not well-defined unless you already have a bunch of samples about potential future outcomes (via, like I mentioned before, a Monte-Carlo type approach. This would multiply the cost of something as simple as an evaluation by many times.)
The measure of uncertainty could be as simple as a multi armed bandit where the reward is % of accurate guesses following that path.
The pseudo adversarial aspect of this being that both models will learn to agree on how choices translate to gameplay changes. The bot will not learn to beat the game necessarily, but a Style GANN kind of approach would probably be just fine to saying, "Please get to a state that looks like this" with a few manual samples. Perhaps all of this to say, it's better to learn how to play the game, and specify that, in this particular run, you'd like the bot to try and reach the game end credits.
[1] Model-Based Active Exploration, Shyam et al. 2019, https://arxiv.org/abs/1810.12162