Topic: value learning

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that depend upon evaluation of errors in performance and reward prediction errors, respectively. In mammals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction errors; but there has been little consideration of how direct policy learning might inform our understanding. I’ll discuss our re