# Multi-agent Task-Driven Exploration via Intelligent Map Compression and Sharing

arXiv: https://arxiv.org/abs/2403.14780 · HTML: https://mlsanity.com/paper/2403.14780/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search](https://mlsanity.com/paper/2412.20049/) (2024)
- [Communication-aware Hierarchical Map Compression of Time-Varying Environments for Mobile Robots](https://mlsanity.com/paper/2504.10751/) (2025)

## Abstract (arXiv)

This paper investigates the task-driven exploration of unknown environments with mobile sensors communicating compressed measurements. The sensors explore the area and transmit their compressed data to another robot, assisting it to reach its goal location. We propose a novel communication framework and a tractable multi-agent exploration algorithm to select the sensors' actions. The algorithm uses a task-driven measure of uncertainty, resulting from map compression, as a reward function. We validate the efficacy of our algorithm through numerical simulations conducted on a realistic map and compare it with alternative approaches. The results indicate that the proposed algorithm effectively decreases the time required for the robot to reach its target without causing excessive load on the communication network.