|
|
|
|
|
|
by moosinho
2784 days ago
|
|
|
How about an approach where the agent's reward is not the predictability itself but the first derivative of it. This way the agent will be attracted to the parts of environment where it can improve and will avoid white-noise parts since its model of the world doesn't generalize on these. Juergen Schmidhuber (the author of original LSTM paper) had a very similar idea, http://people.idsia.ch/~juergen/driven2009.pdf "This drive maximizes interestingness, the
first derivative of subjective beauty or compressibility, that is, the steepness of the
learning curve. It motivates exploring infants, pure mathematicians, composers,
artists, dancers, comedians, yourself, and (since 1990) artificial systems." |
|
|