Important to note: the brain uses reinforcement and reward differently. The brain is primarily based on associative learning (Hebbian learning: neurons that fire together wire together) and the reward/surprise signals act like optional modifiers to the learning rate. Generally speaking, events that are surprisingly rewarding or punishing cause temporary increase in the rate of the formation of the associations. Since we’re talking about trying to translate brain learning functions to similar-in-effect-but-different-in-mechanism machine learning methods, we should try to be clear about human brain terms and ML terms. Sometimes these terms have been borrowed from neuroscience but applied to not quite right ML concepts. Having a unified jargon seems important for the accurate translation of functions.
Important to note: the brain uses reinforcement and reward differently. The brain is primarily based on associative learning (Hebbian learning: neurons that fire together wire together) and the reward/surprise signals act like optional modifiers to the learning rate. Generally speaking, events that are surprisingly rewarding or punishing cause temporary increase in the rate of the formation of the associations. Since we’re talking about trying to translate brain learning functions to similar-in-effect-but-different-in-mechanism machine learning methods, we should try to be clear about human brain terms and ML terms. Sometimes these terms have been borrowed from neuroscience but applied to not quite right ML concepts. Having a unified jargon seems important for the accurate translation of functions.