  We consider large-scale Markov decision processes (MDPs) with a risk measure
of variability in cost, under the risk-aware MDPs paradigm. Previous studies
showed that risk-aware MDPs, based on a minimax approach to handling risk, can
be solved using dynamic programming for small to medium sized problems.
However, due to the "curse of dimensionality", MDPs that model real-life
problems are typically prohibitively large for such approaches. In this paper,
we employ an approximate dynamic programming approach, and develop a family of
simulation-based algorithms to approximately solve large-scale risk-aware MDPs.
In parallel, we develop a unified convergence analysis technique to derive
sample complexity bounds for this new family of algorithms.
