Estimation of Hidden Markov Models for Partially Observed Risk Sensitive Control Problems

Abstract

We look at the problem of estimation for partially observed, risk-sensitive control problems with finite state, input and output sets, and receding horizon. We describe architectures for risk sensitive controllers, and estimation, and we state conditions under which both the estimated model converges to the true model, and the control policy will converge to the optimal risk sensitive policy.