Risk-Averse Markov Decision Processes through a Distributional Lens

Understand

By adopting a distributional viewpoint on law-invariant convex risk measures, we construct dynamics risk measures (DRMs) at the distributional level.

• We then apply these DRMs to investigate Markov decision processes, incorporating latent costs, random actions, and weakly continuous transition kernels.
• Furthermore, the proposed DRMs allow risk aversion to change dynamically.
• Under mild assumptions, we derive a dynamic programming principle and show the existence of an optimal policy in both finite and infinite time horizons.