• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.11-19
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• 2024

Multi-agent systems can be utilized in various real-world cooperative environments such as battlefield engagements and unmanned transport vehicles. In the context of battlefield engagements, where dense reward design faces challenges due to limited domain knowledge, it is crucial to consider situations that are learned through explicit sparse rewards. This paper explores the collaborative potential among allied agents in a battlefield scenario. Utilizing the Multi-Robot Warehouse Environment(RWARE) as a sparse reward environment, we define analogous problems and establish evaluation criteria. Constructing a learning environment with the QMIX algorithm from the reinforcement learning library Ray RLlib, we enhance the Agent Network of QMIX and integrate Random Network Distillation(RND). This enables the extraction of patterns and temporal features from partial observations of agents, confirming the potential for improving the acquisition of sparse reward experiences through intrinsic rewards.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.1
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• pp.47-57
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• 2022

UAVs have been deployed various missions such as deception, reconnaissance and attack since they have been applied in battlefield and achieved missions successfully instead of man. In the past, it is impossible for UAVs to conduct autonomous missions or cooperative mission between manned aircraft due to the limitation of the technology. However, theses missions are possible owing to the advance in communication and AI Technology. In this research, we identified the possible cooperative missions between manned and unmanned team based on air-to-air mission. We studied cooperative manned and unmanned tactics about fighter sweep mission which is the core and basic operation among various air-to-air missions. We also developed cooperative tactics of manned and unmanned team by classifying nonstealth and stealth confrontational tactics. Hereafter, we verified the validity of the suggested tactics using computer simulations.