mlsanityData to 14 Oct 2026

A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO

arXiv 2412.06231 ↗First posted 9 Dec 2024cs.RO cs.LGcited by 0

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

Abstract (arXiv)

The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coordinated operations. In this study, we explore a Recurrent PPO model for target localization in perceptually degraded environments like places without GNSS/GPS signals. We first developed a single-drone approach for target identification, followed by a decentralized two-drone model. Our approach can utilize two types of sensors on the UAVs, a detection sensor and a target signal sensor. The single-drone model achieved an accuracy of 93%, while the two-drone model achieved an accuracy of 86%, with the latter requiring fewer average steps to locate the target. This demonstrates the potential of our method in UAV swarms, offering efficient and effective localization of radiant targets in complex environmental conditions.

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 6 of its 18 references that are in the map. First posted on arXiv 9 Dec 2024.

Builds on

6 of 18 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2022Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimizationcited by 10
  5. 2018A Unified Framework for Joint Mobility Prediction and Object Profiling of Drones in UAV Networkscited by 4
  6. 2018Leader-follower based Coalition Formation in Large-scale UAV Networks, A Quantum Evolutionary Approachcited by 2

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  2. 2026Joint Trajectory, RIS, and Computation Offloading Optimization via Decentralized Model-Based PPO in Urban Multi-UAV Mobile Edge Computing
  3. 2025Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance
  4. 2026Delay-Aware Active Triangulation with Uncertainty-Driven Multi-Agent Reinforcement Learning for Counter-UAS ↗
  5. 2026Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments
  6. 2025SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
  7. 2026Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment
  8. 2024MARLander: A Local Path Planning for Drone Swarms using Multiagent Deep Reinforcement Learning ↗