mlsanityData to 14 Oct 2026

Decentralized End-to-End Multi-AAV Pursuit Using Predictive Spatio-Temporal Observation via Deep Reinforcement Learning

arXiv 2603.24238 ↗First posted 25 Mar 2026cs.ROcited by 0

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

Abstract (arXiv)

Decentralized cooperative pursuit in cluttered environments is challenging for autonomous aerial swarms, especially under partial and noisy perception. Existing methods often rely on abstracted geometric features or privileged ground-truth states, and therefore sidestep perceptual uncertainty in real-world settings. We propose a decentralized end-to-end multi-agent reinforcement learning (MARL) framework that maps raw LiDAR observations directly to continuous control commands. Central to the framework is the Predictive Spatio-Temporal Observation (PSTO), an egocentric grid representation that aligns obstacle geometry with predictive adversarial intent and teammate motion in a unified, fixed-resolution projection. Built on PSTO, a single decentralized policy enables agents to navigate static obstacles, intercept dynamic targets, and maintain cooperative encirclement. Simulations demonstrate that the proposed method achieves superior capture efficiency and competitive success rates compared to state-of-the-art learning-based approaches relying on privileged obstacle information. Furthermore, the unified policy scales seamlessly across different team sizes without retraining. Finally, fully autonomous outdoor experiments validate the framework on a quadrotor swarm relying on only onboard sensing and computing.

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 17 of its 38 references that are in the map. First posted on arXiv 25 Mar 2026.

Builds on

17 of 38 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning24 Sep
  2. 2024NavRL: Learning Safe Flight in Dynamic Environments24 Sep

Other references · 15

  1. 2017Squeeze-and-Excitation Networkscited by 4,789
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  4. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
  5. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  6. 2013The Complexity of Decentralized Control of Markov Decision Processescited by 231
  7. 2019What the Constant Velocity Model Can Teach Us About Pedestrian Motion Predictioncited by 80
  8. 2024Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physicscited by 45
  9. 2023OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Controlcited by 33
  10. 2020Decentralized Multi-Agent Pursuit using Deep Reinforcement Learningcited by 23
  11. 2022RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV Systemcited by 12
  12. 2022Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learningcited by 9
3 more
  1. 2024Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarmscited by 3
  2. 2025Primitive-Swarm: An Ultra-lightweight and Scalable Planner for Large-scale Aerial Swarmscited by 2
  3. 2025Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigationcited by 1

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  1. 2026Communication-Free Collective Navigation for a Swarm of UAVs via LiDAR-Based Deep Reinforcement Learning
  2. 2025Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game
  3. 2026Learning-Based Dynamic Obstacle Avoidance for a UAV Using Only Three Range Sensors
  4. 2025Cooperative Bearing-Only Target Pursuit via Multiagent Reinforcement Learning: Design and Experiment
  5. 2024Dual Agent Learning Based Aerial Trajectory Tracking
  6. 2025Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance
  7. 2026Sensor to Pixels: Decentralized Swarm Gathering via Image-Based Reinforcement Learning
  8. 2024Multi-UAV Formation Control with Static and Dynamic Obstacle Avoidance via Reinforcement Learning