# Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions

arXiv: https://arxiv.org/abs/2110.02181 · HTML: https://mlsanity.com/paper/2110.02181/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2021.

## Built on by (most cited first, 13 of 13)

- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach](https://mlsanity.com/paper/2502.00114/) (2025)
- [Find Everything: A General Vision Language Model Approach to Multi-Object Search](https://mlsanity.com/paper/2410.00388/) (2024)
- [MLLM-Search: A Zero-Shot Approach to Finding People using Multimodal Large Language Models](https://mlsanity.com/paper/2412.00103/) (2024)
- [LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models](https://mlsanity.com/paper/2402.08774/) (2024)
- [4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration](https://mlsanity.com/paper/2402.17904/) (2024)
- [Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.16600/) (2024)
- [Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration](https://mlsanity.com/paper/2408.11751/) (2024)
- [Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search](https://mlsanity.com/paper/2412.20049/) (2024)
- [Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots](https://mlsanity.com/paper/2410.06372/) (2024)
- [Meta-learning how to Share Credit among Macro-Actions](https://mlsanity.com/paper/2506.13690/) (2025)
- [CoCoL: A Communication Efficient Decentralized Collaborative Method for Multi-Robot Systems](https://mlsanity.com/paper/2508.20898/) (2025)
- [Latent Space Reinforcement Learning for Multi-Robot Exploration](https://mlsanity.com/paper/2601.01139/) (2026)

## Abstract (arXiv)

Cooperative multi-robot teams need to be able to explore cluttered and unstructured environments while dealing with communication dropouts that prevent them from exchanging local information to maintain team coordination. Therefore, robots need to consider high-level teammate intentions during action selection. In this letter, we present the first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning (DRL) to address the challenges of communication dropouts during multi-robot exploration in unseen, unstructured, and cluttered environments. Simulated robot team exploration experiments were conducted and compared against classical and DRL methods where MADE-Net outperformed all benchmark methods in terms of computation time, total travel distance, number of local interactions between robots, and exploration rate across various degrees of communication dropouts. A scalability study in 3D environments showed a decrease in exploration time with MADE-Net with increasing team and environment sizes. The experiments presented highlight the effectiveness and robustness of our method.