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Note you can create a game where the simulated world has random variations and the RL algorithm will learn to handle it. If you don't train for it, obviously it won't learn it.

Check out this video from mit/openai: https://www.youtube.com/watch?v=9EN_HoEk3KY The entire talk is interesting but the section at 21:40 talks about "Sim2Real with Meta Learning".

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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.

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