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In AI the definition of reinforcement learning vs unsupervised is that reinforcement requires external identification of the correct result to compare with the
by daveguy 11y ago
In AI the definition of reinforcement learning vs unsupervised is that reinforcement requires external identification of the correct result to compare with the given result.
If going through life you had artificial reality display that flashed "apple" in front of every apple and "orange" in front of every orange you would have reinforcement learning. There is certainly a major component of reinforcement in teaching, but that does not account for all of learning.
With humans we are able to transfer concepts to different situations without examples and there are a lot of categories that are identified without any explicitly reinforced examples.
- zardo 11y agoYou're describing supervised learning. Reinforcement learning is more like you stick random things in your mouth, and if one tastes good, you learn to stick that kind of thing in your mouth more often.
- daveguy 11y agoWell, yes, I was describing supervised learning (as what doesn't happen) where labels are not present is unsupervised (which makes things much more difficult). Reinforcement is more like a special case between supervised and unsupervised. Difficult like unsupervised because the labels aren't known, but with some feedback that is not a direct answer. Sorry for the confusion. There is very little in the way of purely unsupervised learning. I think the distinction between unsupervised and reinforcement is not very well defined. I would argue that even a "pure" unsupervised algorithm like k-means has a kind of reinforcement in the group means.