 Richard S. Sutton

Richard S. Sutton is a computer scientist whose work developed temporal-difference learning, an important method in reinforcement learning. His 1988 paper studied how a predictor can learn from the difference between successive predictions, and proved convergence and optimality in specified special cases.^[ref]

A central idea is to update an earlier estimate using a later estimate before the eventual outcome is known. For example, if an estimated chance rises from $0.4$ to $0.7$, a learning rate of $0.2$ moves the earlier estimate toward the later one by $0.2(0.7-0.4)=0.06$. This illustrates the temporal credit-assignment idea; its guarantees depend on the learning problem and update rule, rather than following from this numerical example alone.