Relative Value Function Approximation
A form of temporal diierence learning is presented that learns the relative utility of states, instead of the absolute utility. This formulation backs up decisions instead of values, making it possible to learn a simpler function for deening a decision-making policy. A nonlinear relative value function can be learned without increasing the dimensionality of the inputs.