Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method
As Unmanned Aerial Vehicle (UAV) technology advances, UAVs have attracted widespread attention across military and civilian fields due to their low cost and flexibility. In unknown environments, UAVs can significantly reduce the risk of casualties and improve the safety and covertness when performin...
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/74 |
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| author | Heng Xu Dayong Zhu |
| author_facet | Heng Xu Dayong Zhu |
| author_sort | Heng Xu |
| collection | DOAJ |
| description | As Unmanned Aerial Vehicle (UAV) technology advances, UAVs have attracted widespread attention across military and civilian fields due to their low cost and flexibility. In unknown environments, UAVs can significantly reduce the risk of casualties and improve the safety and covertness when performing missions. Reinforcement Learning allows agents to learn optimal policies through trials in the environment, enabling UAVs to respond autonomously according to the real-time conditions. Due to the limitation of the observation range of UAV sensors, UAV target search missions face the challenge of partial observation. Based on this, Partially Observable Deep Q-Network (PODQN), which is a DQN-based algorithm is proposed. The PODQN algorithm utilizes the Gated Recurrent Unit (GRU) to remember the past observation information. It integrates the target network and decomposes the action value for better evaluation. In addition, the artificial potential field is introduced to solve the potential collision problem. The simulation environment for UAV target search is constructed through the custom Markov Decision Process. By comparing the PODQN algorithm with random strategy, DQN, Double DQN, Dueling DQN, VDN, QMIX, it is demonstrated that the proposed PODQN algorithm has the best performance under different agent configurations. |
| format | Article |
| id | doaj-art-2648dbead936410a80c2f034e2dfddcb |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Drones |
| spelling | doaj-art-2648dbead936410a80c2f034e2dfddcb2025-01-24T13:29:53ZengMDPI AGDrones2504-446X2025-01-01917410.3390/drones9010074Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable MethodHeng Xu0Dayong Zhu1School of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, ChinaAs Unmanned Aerial Vehicle (UAV) technology advances, UAVs have attracted widespread attention across military and civilian fields due to their low cost and flexibility. In unknown environments, UAVs can significantly reduce the risk of casualties and improve the safety and covertness when performing missions. Reinforcement Learning allows agents to learn optimal policies through trials in the environment, enabling UAVs to respond autonomously according to the real-time conditions. Due to the limitation of the observation range of UAV sensors, UAV target search missions face the challenge of partial observation. Based on this, Partially Observable Deep Q-Network (PODQN), which is a DQN-based algorithm is proposed. The PODQN algorithm utilizes the Gated Recurrent Unit (GRU) to remember the past observation information. It integrates the target network and decomposes the action value for better evaluation. In addition, the artificial potential field is introduced to solve the potential collision problem. The simulation environment for UAV target search is constructed through the custom Markov Decision Process. By comparing the PODQN algorithm with random strategy, DQN, Double DQN, Dueling DQN, VDN, QMIX, it is demonstrated that the proposed PODQN algorithm has the best performance under different agent configurations.https://www.mdpi.com/2504-446X/9/1/74deep q-networkpartially observableunmanned aerial vehiclemulti-agenttarget search |
| spellingShingle | Heng Xu Dayong Zhu Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method Drones deep q-network partially observable unmanned aerial vehicle multi-agent target search |
| title | Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method |
| title_full | Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method |
| title_fullStr | Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method |
| title_full_unstemmed | Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method |
| title_short | Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method |
| title_sort | multiple unmanned aerial vehicle collaborative target search by drl a dqn based multi agent partially observable method |
| topic | deep q-network partially observable unmanned aerial vehicle multi-agent target search |
| url | https://www.mdpi.com/2504-446X/9/1/74 |
| work_keys_str_mv | AT hengxu multipleunmannedaerialvehiclecollaborativetargetsearchbydrladqnbasedmultiagentpartiallyobservablemethod AT dayongzhu multipleunmannedaerialvehiclecollaborativetargetsearchbydrladqnbasedmultiagentpartiallyobservablemethod |