# Multi-Agent Image Classification via Reinforcement Learning

Source: [Multi-Agent Image Classification via Reinforcement Learning](<https://www.sota2.com/research/paper/68fa661b1e209131cea0e557>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-05T05:01:57.872Z

Authors: Hossein K. Mousavi, Mohammadreza Nazari, Martin Tak\\'a\\v{c}, Nader Motee

Published: 2019-05-13T02:24:19.000Z

## Abstract

We investigate a classification problem using multiple mobile agents capable of collecting (partial) pose-dependent observations of an unknown environment. The objective is to classify an image over a finite time horizon. We propose a network architecture on how agents should form a local belief, take local actions, and extract relevant features from their raw partial observations. Agents are allowed to exchange information with their neighboring agents to update their own beliefs. It is shown how reinforcement learning techniques can be utilized to achieve decentralized implementation of the classification problem by running a decentralized consensus protocol. Our experimental results on the MNIST handwritten digit dataset demonstrates the effectiveness of our proposed framework.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1905.04835>)

[PDF](<https://arxiv.org/pdf/1905.04835.pdf>)