mlsanityData to 14 Oct 2026

Multi-source Plume Tracing via Multi-Agent Reinforcement Learning

arXiv 2505.08825 ↗First posted 12 May 2025cs.MA cs.AIcited by 0

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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.

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.

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9 of 54 references.

References · 9

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
  4. 2018On Improving Deep Reinforcement Learning for POMDPscited by 50
  5. 2021Partially Observable Markov Decision Processes (POMDPs) and Roboticscited by 49
  6. 2022A Critical Review of Communications in Multi-Robot Systemscited by 16
  7. 2019Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Cameracited by 8
  8. 2023Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmarkcited by 6
  9. 2019Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environmentscited by 4

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