# Multi-source Plume Tracing via Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2505.08825 · HTML: https://mlsanity.com/paper/2505.08825/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 9 of 9)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [On Improving Deep Reinforcement Learning for POMDPs](https://mlsanity.com/paper/1804.06309/) (2018)
- [Partially Observable Markov Decision Processes (POMDPs) and Robotics](https://mlsanity.com/paper/2107.07599/) (2021)
- [A Critical Review of Communications in Multi-Robot Systems](https://mlsanity.com/paper/2206.09484/) (2022)
- [Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Camera](https://mlsanity.com/paper/1904.08500/) (2019)
- [Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark](https://mlsanity.com/paper/2302.00706/) (2023)
- [Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments](https://mlsanity.com/paper/1912.05684/) (2019)

## Abstract (arXiv)

Industrial catastrophes like the Bhopal disaster (1984) and the Aliso Canyon gas leak (2015) demonstrate the urgent need for rapid and reliable plume tracing algorithms to protect public health and the environment. Traditional methods, such as gradient-based or biologically inspired approaches, often fail in realistic, turbulent conditions. To address these challenges, we present a Multi-Agent Reinforcement Learning (MARL) algorithm designed for localizing multiple airborne pollution sources using a swarm of small uncrewed aerial systems (sUAS). Our method models the problem as a Partially Observable Markov Game (POMG), employing a Long Short-Term Memory (LSTM)-based Action-specific Double Deep Recurrent Q-Network (ADDRQN) that uses full sequences of historical action-observation pairs, effectively approximating latent states. Unlike prior work, we use a general-purpose simulation environment based on the Gaussian Plume Model (GPM), incorporating realistic elements such as a three-dimensional environment, sensor noise, multiple interacting agents, and multiple plume sources. The incorporation of action histories as part of the inputs further enhances the adaptability of our model in complex, partially observable environments. Extensive simulations show that our algorithm significantly outperforms conventional approaches. Specifically, our model allows agents to explore only 1.29% of the environment to successfully locate pollution sources.