# Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2409.16967 · HTML: https://mlsanity.com/paper/2409.16967/

## 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 13 of its 38 references that are in the map.
- First posted on arXiv 25 Sep 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, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control](https://mlsanity.com/paper/2103.02142/) (2021)
- [Next-Best View Policy for 3D Reconstruction](https://mlsanity.com/paper/2008.12664/) (2020)
- [Efficient Informative Sensing using Multiple Robots](https://mlsanity.com/paper/1401.3462/) (2014)
- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring](https://mlsanity.com/paper/2401.04631/) (2024)
- [Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning](https://mlsanity.com/paper/2402.04894/) (2024)
- [Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms](https://mlsanity.com/paper/1302.0723/) (2013)
- [Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2209.07660/) (2022)
- [Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning](https://mlsanity.com/paper/2303.01150/) (2023)
- [IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity](https://mlsanity.com/paper/2409.04730/) (2024)
- [Riemannian Optimization for Active Mapping with Robot Teams](https://mlsanity.com/paper/2404.18321/) (2024)
- [Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning](https://mlsanity.com/paper/2303.05351/) (2023)

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

- [MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation](https://mlsanity.com/paper/2607.15589/) (2026)

## Abstract (arXiv)

Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms of the number of robots deployed in more complex environments. These tasks belong to the set of Multi-Robot Informative Path Planning (MRIPP) problems. In this paper, we propose a deep reinforcement learning approach for the MRIPP problem. We aim to maximize the number of discovered stationary targets in an unknown 3D environment while operating under resource constraints (such as path length). Here, each robot aims to maximize discovered targets, avoid unknown static obstacles, and prevent inter-robot collisions while operating under communication and resource constraints. We utilize the centralized training and decentralized execution paradigm to train a single policy neural network. A key aspect of our approach is our coordination graph that prioritizes visiting regions not yet explored by other robots. Our learned policy can be copied onto any number of robots for deployment in more complex environments not seen during training. Our approach outperforms state-of-the-art approaches by at least 26.2% in terms of the number of discovered targets while requiring a planning time of less than 2 sec per step. We present results for more complex environments with up to 64 robots and compare success rates against baseline planners. Our code and trained model are available at - https://github.com/AccGen99/marl_ipp