# Optimal control in Markov decision processes via distributed optimization

arXiv: https://arxiv.org/abs/1503.07189 · HTML: https://mlsanity.com/paper/1503.07189/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2015.

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

- [Federated Control in Markov Decision Processes](https://mlsanity.com/paper/2405.04026/) (2024)

## Abstract (arXiv)

Optimal control synthesis in stochastic systems with respect to quantitative temporal logic constraints can be formulated as linear programming problems. However, centralized synthesis algorithms do not scale to many practical systems. To tackle this issue, we propose a decomposition-based distributed synthesis algorithm. By decomposing a large-scale stochastic system modeled as a Markov decision process into a collection of interacting sub-systems, the original control problem is formulated as a linear programming problem with a sparse constraint matrix, which can be solved through distributed optimization methods. Additionally, we propose a decomposition algorithm which automatically exploits, if exists, the modular structure in a given large-scale system. We illustrate the proposed methods through robotic motion planning examples.