TY - GEN
T1 - Robustness to Incorrect Models in Average-Cost Optimal Stochastic Control
AU - Kara, Ali Devran
AU - Raginsky, Maxim
AU - Yuksel, Serdar
N1 - This research was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
PY - 2019/12
Y1 - 2019/12
N2 - We study continuity properties of infinite-horizon average expected cost problems with respect to transition probabilities, as well as applications of these results to the problem of robustness of control policies designed for incorrect models applied to systems with incomplete models. We show that sufficient conditions presented in the literature for discounted-cost problems are not sufficient to ensure robustness for averagecost problems. However, we show that the average optimal cost is continuous under the convergence in total variation and in weak convergence in addition to uniform ergodicity and regularity conditions. Using such continuity results, we establish that the mismatch error due to the application of a control policy designed for an incorrectly estimated model is continuous in terms of total variation distance or any weak convergence inducing metric between the true model and an incorrect one, thus leading to robustness.
AB - We study continuity properties of infinite-horizon average expected cost problems with respect to transition probabilities, as well as applications of these results to the problem of robustness of control policies designed for incorrect models applied to systems with incomplete models. We show that sufficient conditions presented in the literature for discounted-cost problems are not sufficient to ensure robustness for averagecost problems. However, we show that the average optimal cost is continuous under the convergence in total variation and in weak convergence in addition to uniform ergodicity and regularity conditions. Using such continuity results, we establish that the mismatch error due to the application of a control policy designed for an incorrectly estimated model is continuous in terms of total variation distance or any weak convergence inducing metric between the true model and an incorrect one, thus leading to robustness.
UR - https://www.scopus.com/pages/publications/85082484515
UR - https://www.scopus.com/pages/publications/85082484515#tab=citedBy
U2 - 10.1109/CDC40024.2019.9029801
DO - 10.1109/CDC40024.2019.9029801
M3 - Conference contribution
AN - SCOPUS:85082484515
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 7970
EP - 7975
BT - 2019 IEEE 58th Conference on Decision and Control, CDC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 58th IEEE Conference on Decision and Control, CDC 2019
Y2 - 11 December 2019 through 13 December 2019
ER -