# Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search

arXiv: https://arxiv.org/abs/2412.20049 · HTML: https://mlsanity.com/paper/2412.20049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 7 of 7)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Heterogeneous-Agent Reinforcement Learning](https://mlsanity.com/paper/2304.09870/) (2023)
- [ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration](https://mlsanity.com/paper/2301.11575/) (2023)
- [Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions](https://mlsanity.com/paper/2110.02181/) (2021)
- [Centralized Model and Exploration Policy for Multi-Agent RL](https://mlsanity.com/paper/2107.06434/) (2021)
- [Multi-agent Task-Driven Exploration via Intelligent Map Compression and Sharing](https://mlsanity.com/paper/2403.14780/) (2024)

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

- [A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration](https://mlsanity.com/paper/2504.11907/) (2025)

## Abstract (arXiv)

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from A^* algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems.