# Verification of Markov Decision Processes with Risk-Sensitive Measures

arXiv: https://arxiv.org/abs/1803.00091 · HTML: https://mlsanity.com/paper/1803.00091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning Beyond Expectation](https://mlsanity.com/paper/2104.00540/) (2021)

## Abstract (arXiv)

We develop a method for computing policies in Markov decision processes with risk-sensitive measures subject to temporal logic constraints. Specifically, we use a particular risk-sensitive measure from cumulative prospect theory, which has been previously adopted in psychology and economics. The nonlinear transformation of the probabilities and utility functions yields a nonlinear programming problem, which makes computation of optimal policies typically challenging. We show that this nonlinear weighting function can be accurately approximated by the difference of two convex functions. This observation enables efficient policy computation using convex-concave programming. We demonstrate the effectiveness of the approach on several scenarios.