# Multi-Agent Image Classification via Reinforcement Learning

arXiv: https://arxiv.org/abs/1905.04835 · HTML: https://mlsanity.com/paper/1905.04835/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 8 of 8)

- [Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems](https://mlsanity.com/paper/2109.12343/) (2021)
- [Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems](https://mlsanity.com/paper/2206.10158/) (2022)
- [Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input](https://mlsanity.com/paper/2203.05403/) (2022)
- [FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences](https://mlsanity.com/paper/2210.09626/) (2022)
- [Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery](https://mlsanity.com/paper/2308.13801/) (2023)
- [The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis](https://mlsanity.com/paper/2507.12473/) (2025)
- [StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigm](https://mlsanity.com/paper/2512.16444/) (2025)
- [Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement](https://mlsanity.com/paper/2604.22110/) (2026)

## Abstract (arXiv)

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.