mlsanityData to 14 Oct 2026

Decentralized Multi-Agent Pursuit using Deep Reinforcement Learning

arXiv 2010.08193 ↗First posted 16 Oct 2020cs.MAcited by 23

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Abstract (arXiv)

Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omni-directional target with multiple, homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given number of pursuers that is executed independently by each agent at run-time. The training benefits from curriculum learning, a sweeping-angle ordering to locally represent neighboring agents and encouraging good formations with reward structure that combines individual and group rewards. Simulated experiments with a reactive evader and up to eight pursuers show that our learning-based approach, with non-holonomic agents, performs on par with classical algorithms with omni-directional agents, and outperforms their non-holonomic adaptations. The learned policy is successfully transferred to the real world in a proof-of-concept demonstration with three motion-constrained pursuer drones.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Oct 2020.

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Citing papers per year: 2021: 1; 2022: 3; 2023: 5; 2024: 6; 2025: 6; 2026: 2.

23 papers.

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  1. 2022Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learningcited by 9
  2. 2023Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Dronescited by 9
  3. 2023ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learningcited by 9
  4. 2024Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial Gamescited by 7
  5. 2022 T3 OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Areacited by 5
  6. 2024Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learningcited by 4
  7. 2025Learned Controllers for Agile Quadrotors in Pursuit-Evasion Gamescited by 3
  8. 2022Toward multi-target self-organizing pursuit in a partially observable Markov gamecited by 2
  9. 2024Hierarchical Reinforcement Learning for Swarm Confrontation with High Uncertaintycited by 2
  10. 2023A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environmentscited by 1
  11. 2024HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuitcited by 1
  12. 2025TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement Learningcited by 1
11 more
  1. 2025Curriculum Imitation Learning of Distributed Multi-Robot Policiescited by 1
  2. 2021Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learningcited by 0
  3. 2023Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuitcited by 0
  4. 2023Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problemcited by 0
  5. 2024Multi-Robot Pursuit in Parameterized Formation via Imitation Learningcited by 0
  6. 2024TRUST-UP: Trustworthy Reinforcement learning Using Safe Techniques for UAV Pursuitcited by 0
  7. 2025AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuitcited by 0
  8. 2025ARBoids: Adaptive Residual Reinforcement Learning With Boids Model for Cooperative Multi-USV Target Defensecited by 0
  9. 2025Strategic Communication under Threat: Learning Information Trade-offs in Pursuit-Evasion Gamescited by 0
  10. 2026Decentralized End-to-End Multi-AAV Pursuit Using Predictive Spatio-Temporal Observation via Deep Reinforcement Learningcited by 0
  11. 2026M2GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuitcited by 0