# An Actor-Critic Algorithm with Function Approximation for Risk Sensitive Cost Markov Decision Processes

arXiv: https://arxiv.org/abs/2502.11604 · HTML: https://mlsanity.com/paper/2502.11604/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 2 of 2)

- [Entropic Risk Measure in Policy Search](https://mlsanity.com/paper/1906.09090/) (2019)
- [A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP](https://mlsanity.com/paper/2202.04157/) (2022)

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

- [Markov Decision Processes of the Third Kind: Learning Distributions by Policy Gradient Descent](https://mlsanity.com/paper/2602.06567/) (2026)

## Abstract (arXiv)

In this paper, we consider the risk-sensitive cost criterion with exponentiated costs for Markov decision processes and develop a model-free policy gradient algorithm in this setting. Unlike additive cost criteria such as average or discounted cost, the risk-sensitive cost criterion is less studied due to the complexity resulting from the multiplicative structure of the resulting Bellman equation. We develop an actor-critic algorithm with function approximation in this setting and provide its asymptotic convergence analysis. We also show the results of numerical experiments that demonstrate the superiority in performance of our algorithm over other recent algorithms in the literature.