This tells me the algo is trying to hard to predict the game or learn a decent static strategy, rather than make situational decisions.
The advantage of human common sense over machine learning models — at least when it comes to role-playing games — is that we carry around a ton of this cultural information. A model trained only on NetHack — not on broader culture or folklore/fairytales/mythology/fantasy — is simply not going to be aware of this link between full moons and specific monsters becoming more dangerous. So if it’s developed a fairly naïve strategy of just fighting or avoiding everything in its path based on a model of relative strength then it’s going to be tripped up when an outside event (the phase of the moon) upends that model.
bet it comes down to how much memory the algorithm has, since the transformation might occur way later than being bitten, while most poison kills are fairly quick. The problem is NetHack requires you to have at minimum 1000 turns of memory to know when to pray. Even more if you want to keep track of where stuff was.
Wouldn’t be very surprising if the agent hyper-optimised farming those critters for points. It would not be able to change strategy if the cost/benefit of that farming changed massively, so would now be performing significantly worse.