Dynamic Programming and Stochastic Control

Abstract

Sequential decision-making via dynamic programming. Unified approach to optimal control of stochastic dynamic systems and Markovian decision problems. Applications in linear-quadratic control, inventory control, and resource allocation models. Optimal decision making under perfect and imperfect state information. Certainty equivalent and open loop-feedback control, and self-tuning controllers. Infinite horizon problems, successive approximation, and policy iteration. Discounted problems, stochastic shortest path problems, and average cost problems. Optimal stopping, scheduling, and control of queues. Approximations and neurodynamic programming. From the course home page: Course Description This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). Approximation methods for problems involving large state spaces are also presented and discussed.