Generalized Dual Dynamic Programming for Infinite Horizon Problems in Continuous State and Action Spaces

Abstract

We describe a nonlinear generalization of dual dynamic programming (DP) theory and its application to value function estimation for deterministic control problems over continuous state and action spaces, in a discrete-time infinite horizon setting. We prove, using a Benders-type argument leveraging the monotonicity of the Bellman operator, that the result of a one-stage policy evaluation can be used to produce nonlinear lower bounds on the optimal value function that are valid over the entire state space. These bounds contain terms reflecting the functional form of the system's costs, dynamics, and constraints. We provide an iterative algorithm that produces successively better approximations of the optimal value function, and prove under certain assumptions that it achieves an arbitrarily low desired Bellman optimality tolerance at preselected points in the state space, in a finite number of iterations. We also describe means of certifying the quality of the approximate value function generated. We demonstrate the efficacy of the approach on systems whose dimensions are too large for conventional DP approaches to be practical.