Stochastic Optimal Control Formulations of Decision Problems

Abstract In this article, we give a brief overview of different stochastic optimal control problems that arise in decision problems. An informal derivation of the Hamilton–Jacobi–Bellman equation, which characterizes the optimal policy, is derived for the case of full state infinite horizon optimal control problem. We then discuss the optimal control problem where the horizon is governed by a random stopping time. An example illustrating the application of the theory to a portfolio selection problem is provided. After a brief discussion on ergodic and risk sensitive control formulations, we move to the case when the state is partially observed. We then state the key separation result in this case and provide some intuitive explanation.