It's not at all obvious that "if you don't train for it, it won't learn it". Humans do not at all learn in this way: we are very good at adapting existing knowledge to solve new tasks, and relatedly at learning new tasks from very little data. This challenge is a major issue with deep reinforcement learning (and maybe deep learning more broadly). It's unclear how we might surmount this problem, but I believe it'll involve some combination of model-based approaches and deep learning models that internally use more symbolic structures.