Approximate dynamic programming via direct search in the space of value function approximations

This paper deals with approximate value iteration (AVI) algorithms applied to discounted dynamic programming (DP) problems. For a fixed control policy, the span semi‐norm of the so‐called Bellman residual is shown to be convex in the Banach space of candidate solutions to the DP problem. This fact motivates the introduction of an AVI algorithm with local search that seeks to minimize the span semi‐norm of the Bellman residual in a convex value function approximation space. The novelty here is that the optimality of a point in the approximation architecture is characterized by means of convex optimization concepts and necessary and sufficient conditions to local optimality are derived. The procedure employs the classical AVI algorithm direction (Bellman residual) combined with a set of independent search directions, to improve the convergence rate. It has guaranteed convergence and satisfies, at least, the necessary optimality conditions over a prescribed set of directions. To illustrate the method, examples are presented that deal with a class of problems from the literature and a large state space queueing problem setting.