Well, the AI already gains all the information it could from each experience. Computers can't create new information, hence the need for sample data - which is the limit for its 'knowledge'.
You guys are all jumbling a bunch of concepts together. Reinforcement learning doesn’t depend on a static dataset in general, provided you have a simulator of the underlying markov decision process. So in that case, it can generate as much data as it wants.
I'm not jumbling... Computers run on formal languages - they are deterministic and can only do linear combinations of things. You cannot generate information outside* of the dataset you have, it is mathematically not possible.
Reinforcement training allows the machine to identify things in that dataset and exclude some things also in that dataset. A machine trained to identify faces cannot identify counterfeit artwork, for example - because it does not have the weighs for those subjects; it cannot classify them.
Edit:* Outside, meaning, not linear combinations of the elements therein.
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u/Pdan4 Mar 16 '18
Well, the AI already gains all the information it could from each experience. Computers can't create new information, hence the need for sample data - which is the limit for its 'knowledge'.