# Decentralized Multi-agent Filtering

arXiv: https://arxiv.org/abs/2301.08864 · HTML: https://mlsanity.com/paper/2301.08864/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 21 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates](https://mlsanity.com/paper/2101.05436/) (2021)
- [Multi-agent estimation and filtering for minimizing team mean-squared error](https://mlsanity.com/paper/1903.12018/) (2019)
- [Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control](https://mlsanity.com/paper/2009.09361/) (2020)

## Abstract (arXiv)

This paper addresses the considerations that comes along with adopting decentralized communication for multi-agent localization applications in discrete state spaces. In this framework, we extend the original formulation of the Bayes filter, a foundational probabilistic tool for discrete state estimation, by appending a step of greedy belief sharing as a method to propagate information and improve local estimates' posteriors. We apply our work in a model-based multi-agent grid-world setting, where each agent maintains a belief distribution for every agents' state. Our results affirm the utility of our proposed extensions for decentralized collaborative tasks. The code base for this work is available in the following repo