MADRL-based UAV swarm non-cooperative game under incomplete information

WANG, Ershen; LIU, Fan; HONG, Chen; GUO, Jing; ZHAO, Lin; XUE, Jian; HE, Ning

doi:10.1016/j.cja.2024.03.030

Chinese Journal of Aeronautics

2024

DOI: 10.1016/j.cja.2024.03.030

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MADRL-based UAV swarm non-cooperative game under incomplete information

Ershen WANG,

Fan LIU,

Chen HONG

et al.

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2024

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Cited by 2 publications

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Multiple unmanned aerial vehicle coordinated strikes against ground targets based on an improved multi-agent deep deterministic policy gradient algorithm

Li,

Chen,

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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With the development of swarm intelligence, multi-unmanned aerial vehicle (UAV) cooperation in uncertain dynamic environments has been able to safely and efficiently realize all-round strikes on ground targets. However, the limited life span of UAV onboard batteries challenges the cooperative strikes against the ground targets. To address this challenge, this paper proposes an improved multi-agent deep deterministic policy gradient (i-MADDPG) algorithm for energy-saving cooperative strikes of ground targets. Introducing a sensor fusion layer and self-attention mechanism to the actor network helps the UAVs collect more comprehensive information and filter the collected data, resulting in more accurate environmental perception. In addition, importing an egocentric state representation mechanism into the critic network contributes to the computation of different Q-values for different UAV observation states, which ensures each UAV can make appropriate decisions based on its own state. Simulation experiments are conducted to validate the performance of the improved MADDPG algorithm. The results show that the success rate of ground target strikes of the proposed algorithm is improved by 29.5%, and the collision rate is reduced compared with the traditional MADDPG algorithm.

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Multiple unmanned aerial vehicle coordinated strikes against ground targets based on an improved multi-agent deep deterministic policy gradient algorithm

Li,

Chen,

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

Application of Task Allocation Algorithms in Multi-UAV Intelligent Transportation Systems: A Critical Review

Rinaldi,

Wang,

Geronel

et al. 2024

BDCC

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Unmanned aerial vehicles (UAVs), commonly known as drones, are being seen as the most promising type of autonomous vehicles in the context of intelligent transportation system (ITS) technology. A key enabling factor for the current development of ITS technology based on autonomous vehicles is the task allocation architecture. This approach allows tasks to be efficiently assigned to robots of a multi-agent system, taking into account both the robots’ capabilities and service requirements. Consequently, this study provides an overview of the application of drones in ITSs, focusing on the applications of task allocation algorithms for UAV networks. Currently, there are different types of algorithms that are employed for task allocation in drone-based intelligent transportation systems, including market-based approaches, game-theory-based algorithms, optimization-based algorithms, machine learning techniques, and other hybrid methodologies. This paper offers a comprehensive literature review of how such approaches are being utilized to optimize the allocation of tasks in UAV-based ITSs. The main characteristics, constraints, and limitations are detailed to highlight their advantages, current achievements, and applicability to different types of UAV-based ITSs. Current research trends in this field as well as gaps in the literature are also thoughtfully discussed.

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MADRL-based UAV swarm non-cooperative game under incomplete information

Cited by 2 publications

References 43 publications

Multiple unmanned aerial vehicle coordinated strikes against ground targets based on an improved multi-agent deep deterministic policy gradient algorithm

Multiple unmanned aerial vehicle coordinated strikes against ground targets based on an improved multi-agent deep deterministic policy gradient algorithm

Application of Task Allocation Algorithms in Multi-UAV Intelligent Transportation Systems: A Critical Review

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