Toward a Scalable Upper Bound for a CVaR-LQ Problem
Systems and Control
2022-06-28 v4 Systems and Control
Abstract
We study a linear-quadratic, optimal control problem on a discrete, finite time horizon with distributional ambiguity, in which the cost is assessed via Conditional Value-at-Risk (CVaR). We take steps toward deriving a scalable dynamic programming approach to upper-bound the optimal value function for this problem. This dynamic program yields a novel, tunable risk-averse control policy, which we compare to existing state-of-the-art methods.
Cite
@article{arxiv.2103.02136,
title = {Toward a Scalable Upper Bound for a CVaR-LQ Problem},
author = {Margaret P. Chapman and Laurent Lessard},
journal= {arXiv preprint arXiv:2103.02136},
year = {2022}
}
Comments
This version of the article makes almost-everywhere notions explicit (Lemma 3, Theorem 2)