Collaborative Decision-Making Method for Multi-UAV Based on Multiagent Reinforcement Learning

Li, Shaowei; Jia, Yingmin; Yang, Fan; Qin, Qingyang; Gao, Hui; Zhou, Yaoming

doi:10.1109/access.2022.3199070

Cited by 27 publications

(18 citation statements)

References 28 publications

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“…Merkezi eleştiriyi tasarlamak için bir dikkat mekanizması kullanılmaktadır. Algoritma eğitilir ve test edilir Çoklu İHA işbirlikçi karar verme problemini çözmek için, çok erkinli bir pekiştirmeli öğrenme tasarlandı [6].…”

Section: çOk Ekinli Sistemlerunclassified

Çoklu İHA ile Çok Erkinli Sistemler

AKÇAY

2022

EJOSAT

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Bu çalışmada birden fazla insansız hava aracının (İHA) kontrolünde, haberleşmesinde çok erkinli sistem yaklaşımında özetlenecektir. Çoklu İHA sistemleri son yıllarda birçok alanda askeri ve sivil uygulamalarda kullanılmaktadır. Çoklu İHA sistemleri kullanılarak karar vermede çok erkinli sistem yaklaşımı açıklanacaktır. Ortamın haritasının çıkarılması, verilen görevin çoklu İHA kullanılarak çözülmesinde çok erkin sistem yapısı incelenecektir. Çoklu İHA kullanılarak elde edilen sonuçlar tartışılacaktır.

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Section: çOk Ekinli Sistemlerunclassified

Çoklu İHA ile Çok Erkinli Sistemler

AKÇAY

2022

EJOSAT

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“…Chai et al [8] utilized two cascade DRL controllers to output the desired attitude and control command respectively. Moreover, techniques such as transformer [9] and inverse DRL [10] were also applied.…”

Section: Introductionmentioning

confidence: 99%

“…However, most works concerning DRL-based dogfight [9]- [14] only focused on simplified UCAV dynamics with three degrees of freedom (DOF), which is far from the real situation. Moreover, most works only claimed their algorithms outperformed conventional methods.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, most works only claimed their algorithms outperformed conventional methods. Some simple reasons were given, such as "more aggressive" [4], [7]- [9], but a deep interpretation of how DRL agent performs is still lacking. In response to these problems, this paper aims to enhance the interpretability of DRL behaviors when facing dogfights under realistic 6-DOF dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable DRL-based Maneuver Decision of UCAV Dogfight

Han,

Cheng,

2024

Preprint

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This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of "Dive and Chase" behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent.

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“…On the other hand, the CU is typically situated at the traditional base stations (BS), housing the centralized processing and control functions. This segregation of responsibilities allows for a more distributed and flexible network architecture, enabling UAVs to efficiently handle communication tasks at the edge of the network, while the centralized units manage core functionalities and resource allocation [5]. This paper addresses the challenge of optimizing end-to-end performance in UAV-assisted cellular networks, focusing on the dynamic and adaptive 3D placement of UAVs.…”

Section: Introductionmentioning

confidence: 99%

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

Wang

Deng

et al. 2022

IEEE Trans. Commun.

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The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.

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Collaborative Decision-Making Method for Multi-UAV Based on Multiagent Reinforcement Learning

Cited by 27 publications

References 28 publications

Çoklu İHA ile Çok Erkinli Sistemler

Çoklu İHA ile Çok Erkinli Sistemler

Interpretable DRL-based Maneuver Decision of UCAV Dogfight

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

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