  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.
