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A Curriculum-Learning-Assisted MAPPO-Based Algorithm for Dynamic Spectrum Access and Anti-Jamming in UAV Swarms.

Xiaoze Yuan1, Jiabao Wen1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
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This study introduces a new dynamic access algorithm for drone swarms, improving communication reliability in complex environments. The Curriculum Learning-assisted Multi-Agent Proximal Policy Optimization (CL-MAPPO) method enhances anti-jamming capabilities and efficiency.

Area of Science:

  • Robotics and Communication Systems
  • Artificial Intelligence and Machine Learning

Background:

  • Drone swarms require high-concurrency, reliable communication, which is challenging in complex environments.
  • Traditional Medium Access Control (MAC) protocols and deep reinforcement learning (DRL) face limitations in collision handling, convergence, and anti-jamming robustness.

Purpose of the Study:

  • To propose a novel dynamic access algorithm for enhancing drone swarm communication reliability.
  • To address the limitations of existing methods in high-density collision scenarios and non-stationary interference.

Main Methods:

  • A Centralized Training with Decentralized Execution (CTDE) architecture is employed for implicit spectrum cooperation.
  • A three-stage curriculum learning mechanism (collision avoidance, load balancing, anti-jamming) with phased reward reshaping guides agent learning.
Keywords:
centralized training with decentralized executioncurriculum learningdrone swarmdynamic spectrum accessmulti-agent proximal policy optimizationmulti-agent reinforcement learningunmanned aerial vehicle

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  • The proposed algorithm is Curriculum Learning-assisted Multi-Agent Proximal Policy Optimization (CL-MAPPO).

    • Main Results:

    • CL-MAPPO significantly outperforms baseline models (CSMA, random frequency hopping, MADDPG) in simulated dynamic jamming and high-load scenarios.
    • Demonstrated improvements in normalized throughput and reduced channel collision rates.
    • Achieved faster convergence speeds compared to conventional methods.

    Conclusions:

    • The CL-MAPPO algorithm provides a robust solution for reliable communication in large-scale drone swarms under harsh conditions.
    • Offers theoretical support and an algorithmic foundation for advanced swarm data links.
    • Highlights the effectiveness of curriculum learning in complex multi-agent reinforcement learning tasks.