# Ergodic Risk-sensitive control -- A survey

arXiv: https://arxiv.org/abs/2301.00224 · HTML: https://mlsanity.com/paper/2301.00224/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2022.

## Built on by (most cited first, 4 of 4)

- [Planning and Learning in Average Risk-aware MDPs](https://mlsanity.com/paper/2503.17629/) (2025)
- [Model-Agnostic Solutions for Deep Reinforcement Learning in Non-Ergodic Contexts](https://mlsanity.com/paper/2601.08726/) (2026)
- [Reinforcement learning with an expectile-based objective](https://mlsanity.com/paper/2602.09300/) (2026)
- [Finite-Time Analysis of Discounted Exponential-Utility Reinforcement Learning](https://mlsanity.com/paper/2608.01917/) (2026)

## Abstract (arXiv)

Risk-sensitive control has received considerable interest since the seminal work of Howard and Matheson [120] because of its ability to account for fluctuations about the mean, its connection with H_∞ control, and its application to financial mathematics. In this article, we attempt to put together a comprehensive survey on the research done on ergodic risk-sensitive control over the last four decades.