mlsanityData to 14 Oct 2026

Task-Semantic Graph-Driven Distributed Agent Networking for Underwater Target Tracking

arXiv 2605.15528 ↗First posted 15 May 2026cs.RO cs.MAcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Autonomous underwater vehicle (AUV) swarms are emerging as intelligent underwater networks, where each node must sense, communicate, process local data, and make decisions under severe acoustic constraints. Persistent underwater target tracking is a typical task with moving targets, changing communication topology, intermittent acoustic links, and limited observation for each AUV. Multi-agent reinforcement learning (MARL) is a natural candidate for distributed tracking, yet existing studies still lack a unified open-source platform for evaluating different MARL algorithms under six-degree-of-freedom AUV dynamics. In addition, policies trained with raw geometric states and low-level force actions often struggle to represent task phases, observation reliability, link quality, and local cooperation roles. This paper addresses these issues by developing an open-source MARL-AUV platform that integrates DI-engine with a six-degree-of-freedom underwater AUV target-tracking simulator. To the best of our knowledge, it is the first open platform that connects a public MARL training framework with physically modeled AUV swarm-based tasks, and provides a unified experimental protocol for fair training, testing, and comparison of representative RL and MARL algorithms. Based on this platform, we propose STG-MAPPO, a Semantic Task Graph-enhanced variant of Multi-Agent Proximal Policy Optimization. STG-MAPPO builds semantic policy inputs from tracking diagnostics, task phases, observation confidence, link availability, neighbor tracking quality, and local role advantage. A compact semantic task graph links communication-constrained network states to decentralized actor decisions, and a velocity-level action abstraction maps high-level cooperative decisions to executable six-degree-offreedom AUV control inputs.The code is available at https://github.com/dasjsaj/MARL-AUV.

Key facts. No citing papers in the map yet (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 29 references that are in the map. First posted on arXiv 15 May 2026.

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13 of 29 references.

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  1. 2024Is FISHER All You Need in The Multi-AUV Underwater Target Tracking Task?5 Dec

Other references · 12

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  4. 2020Deep Learning Enabled Semantic Communication Systemscited by 292
  5. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 186
  6. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 184
  7. 2022Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesiscited by 33
  8. 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AIcited by 33
  9. 2022Task-Oriented Over-the-Air Computation for Multi-Device Edge AIcited by 14
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  11. 2024OFDM-Based Digital Semantic Communication with Importance Awarenesscited by 12
  12. 2023Goal-oriented Semantic Communications for Robotic Waypoint Transmission: The Value and Age of Information Approachcited by 5

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  1. 2026Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks
  2. 2026Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach
  3. 2025Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
  4. 2025Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour ↗
  5. 2026Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems
  6. 2025Cooperative Target Detection with AUVs: A Dual-Timescale Hierarchical MARDL Approach
  7. 2025Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams
  8. 2026Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation