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suflaj t1_j6eqh0b wrote

It depends. If it only learned A to B we say it is overfit. If you give it enough different A to Bs, it might learn to generalize, and then for any A to B pair it will be able to find the path.

If it learned on paths without obstacles, it will not be able to deal with obstacles. Which means that it will go right through them, or run into them, if your environment does not alloe an agent to go through them.

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Severe-Improvement32 OP t1_j6ev9gf wrote

Got your point, and have another question: So let continue with path finding example, if we do not give the enough AB pairs the as you said it will fail, But then what about unsupervised learning as there won't be any data given right?

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suflaj t1_j6evpg4 wrote

Well you will presumably not be labeling this with humans but probably Astar, so it's all unsupervised learning anyways

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Autogazer t1_j6h0bi5 wrote

That’s not how unsupervised training works. All training requires data, unsupervised just means that the data isn’t labeled.

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