Approximate value iteration with randomized policies
The curse of dimensionality in dynamic programming prevents, in most problems of practical interest, the exact computation of the value function. We study the fixed points of approximate value iteration, a simple algorithm that combats the curse of dimensionality by generating approximate iterates of the classical value iteration algorithm in the span of a set of prespecified basis functions. We show that, in general, the modified dynamic programming operator need not possess a fixed point, and therefore, approximate value iteration should not be expected to converge. However, by using a class of randomized policies, approximate value iteration is guaranteed to possess at least one fixed point. We finally discuss the link between approximate value iteration and temporal-difference learning (TD), and show that the existence of fixed points for approximate value iteration implies existence of stationary points for the ordinary differential equation approximated by a version of TD that incorporates "exploration".