MW-MADDPG: a meta-learning based decision-making method for collaborative UAV swarm

Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approach...

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Published in:	Frontiers in neurorobotics Vol. 17; p. 1243174
Main Authors:	Zhao, Minrui, Wang, Gang, Fu, Qiang, Guo, Xiangke, Chen, Yu, Li, Tengda, Liu, XiangYu
Format:	Journal Article
Language:	English
Published:	Lausanne Frontiers Research Foundation 21.09.2023 Frontiers Media S.A
Subjects:	Algorithms Cluster analysis Collaboration Decision making Deep learning Efficiency Evacuations & rescues Learning MADDPG meta learning Model Agnostic Meta Learning (MAML) multi-agent reinforcement learning (MARL) Neuroscience Reinforcement Scheduling UAV Unmanned aerial vehicles
ISSN:	1662-5218, 1662-5218
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Abstract	Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approaches are challenged by the multivariate mission environment and mission time constraints. In light of this, the present study proposes a meta-learning based multi-agent deep reinforcement learning approach that provides a viable solution to this problem. This paper presents an improved MAML-based multi-agent deep deterministic policy gradient (MADDPG) algorithm that achieves an unbiased initialization network by automatically assigning weights to meta-learning trajectories. In addition, a Reward-TD prioritized experience replay technique is introduced, which takes into account immediate reward and TD-error to improve the resilience and sample utilization of the algorithm. Experiment results show that the proposed approach effectively accomplishes the task in the new scenario, with significantly improved task success rate, average reward, and robustness compared to existing methods.
AbstractList	Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approaches are challenged by the multivariate mission environment and mission time constraints. In light of this, the present study proposes a meta-learning based multi-agent deep reinforcement learning approach that provides a viable solution to this problem. This paper presents an improved MAML-based multi-agent deep deterministic policy gradient (MADDPG) algorithm that achieves an unbiased initialization network by automatically assigning weights to meta-learning trajectories. In addition, a Reward-TD prioritized experience replay technique is introduced, which takes into account immediate reward and TD-error to improve the resilience and sample utilization of the algorithm. Experiment results show that the proposed approach effectively accomplishes the task in the new scenario, with significantly improved task success rate, average reward, and robustness compared to existing methods. Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approaches are challenged by the multivariate mission environment and mission time constraints. In light of this, the present study proposes a meta-learning based multi-agent deep reinforcement learning approach that provides a viable solution to this problem. This paper presents an improved MAML-based multi-agent deep deterministic policy gradient (MADDPG) algorithm that achieves an unbiased initialization network by automatically assigning weights to meta-learning trajectories. In addition, a Reward-TD prioritized experience replay technique is introduced, which takes into account immediate reward and TD-error to improve the resilience and sample utilization of the algorithm. Experiment results show that the proposed approach effectively accomplishes the task in the new scenario, with significantly improved task success rate, average reward, and robustness compared to existing methods.Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approaches are challenged by the multivariate mission environment and mission time constraints. In light of this, the present study proposes a meta-learning based multi-agent deep reinforcement learning approach that provides a viable solution to this problem. This paper presents an improved MAML-based multi-agent deep deterministic policy gradient (MADDPG) algorithm that achieves an unbiased initialization network by automatically assigning weights to meta-learning trajectories. In addition, a Reward-TD prioritized experience replay technique is introduced, which takes into account immediate reward and TD-error to improve the resilience and sample utilization of the algorithm. Experiment results show that the proposed approach effectively accomplishes the task in the new scenario, with significantly improved task success rate, average reward, and robustness compared to existing methods.
Author	Liu, XiangYu Guo, Xiangke Wang, Gang Zhao, Minrui Fu, Qiang Chen, Yu Li, Tengda
AuthorAffiliation	2 Graduate School, Academy of Military Science , Beijing , China 1 College of Air and Missile Defense, Air Force Engineering University , Xi'an , China 3 Unit 95866 of PLA , Baoding , China
AuthorAffiliation_xml	– name: 3 Unit 95866 of PLA , Baoding , China – name: 1 College of Air and Missile Defense, Air Force Engineering University , Xi'an , China – name: 2 Graduate School, Academy of Military Science , Beijing , China
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SubjectTerms	Algorithms Cluster analysis Collaboration Decision making Deep learning Efficiency Evacuations & rescues Learning MADDPG meta learning Model Agnostic Meta Learning (MAML) multi-agent reinforcement learning (MARL) Neuroscience Reinforcement Scheduling UAV Unmanned aerial vehicles
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Title	MW-MADDPG: a meta-learning based decision-making method for collaborative UAV swarm
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