In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty.

Using this information, we would like to solve the optimization problem (1).

The latter allows us to control the error in the stochastic gradient updates due to inaccurate solution to both subproblems.

A natural idea is to compute the solution path w.r.t.