# Structural Results for Partially Observed Markov Decision Processes

arXiv: https://arxiv.org/abs/1512.03873 · HTML: https://mlsanity.com/paper/1512.03873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2015.

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

- [Reinforcement Learning Design for Quickest Change Detection](https://mlsanity.com/paper/2403.14109/) (2024)

## Abstract (arXiv)

This article provides an introductory tutorial on structural results in partially observed Markov decision processes (POMDPs). Typically, computing the optimal policy of a POMDP is computationally intractable. We use lattice program- ming methods to characterize the structure of the optimal policy of a POMDP without brute force computations.