mlsanityData to 14 Oct 2026

Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game

arXiv 2506.18126 ↗First posted 22 Jun 2025cs.AIcited by 2

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

Abstract (arXiv)

Multiple quadrotor unmanned aerial vehicle (UAV) systems have garnered widespread research interest and fostered tremendous interesting applications, especially in multi-constrained pursuit-evasion games (MC-PEG). The Cooperative Evasion and Formation Coverage (CEFC) task, where the UAV swarm aims to maximize formation coverage across multiple target zones while collaboratively evading predators, belongs to one of the most challenging issues in MC-PEG, especially under communication-limited constraints. This multifaceted problem, which intertwines responses to obstacles, adversaries, target zones, and formation dynamics, brings up significant high-dimensional complications in locating a solution. In this paper, we propose a novel two-level framework (i.e., Consensus Inference-based Hierarchical Reinforcement Learning (CI-HRL)), which delegates target localization to a high-level policy, while adopting a low-level policy to manage obstacle avoidance, navigation, and formation. Specifically, in the high-level policy, we develop a novel multi-agent reinforcement learning module, Consensus-oriented Multi-Agent Communication (ConsMAC), to enable agents to perceive global information and establish consensus from local states by effectively aggregating neighbor messages. Meanwhile, we leverage an Alternative Training-based Multi-agent proximal policy optimization (AT-M) and policy distillation to accomplish the low-level control. The experimental results, including the high-fidelity software-in-the-loop (SITL) simulations, validate that CI-HRL provides a superior solution with enhanced swarm's collaborative evasion and task completion capabilities.

Key facts. Cited by 2 papers in the map (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 23 of its 58 references that are in the map. First posted on arXiv 22 Jun 2025.

Builds on

23 of 58 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms20 Jan

Other references · 22

  1. 2017Attention Is All You Needcited by 60,361
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10 more
  1. 2021ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mindcited by 58
  2. 2023Maximum Entropy Heterogeneous-Agent Reinforcement Learningcited by 19
  3. 2023Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackerscited by 15
  4. 2024Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Surveycited by 13
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Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarmscited by 7
  2. 2025LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approachcited by 0

Similar, not cited

Close in wording, no citation either way

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  3. 2025Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks
  4. 2025A Hierarchical Reinforcement Learning Framework for Multi-UAV Combat Using Leader-Follower Strategy
  5. 2026Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters
  6. 2025From Static to Adaptive Defense: Federated Multi-Agent Deep Reinforcement Learning-Driven Moving Target Defense Against DoS Attacks in UAV Swarm Networks
  7. 2025A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge
  8. 2025Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms