• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.79-88
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• 2024

With the advancement of unmanned aerial vehicle technology, swarm drones are increasingly being deployed across various domains, including disaster response and military operations, where their effectiveness is particularly pronounced. These swarm drones leverage real-time data sharing and decision-making capabilities to execute tactical missions, making collaborative behavior essential in complex battlefield environments. However, traditional rule-based behavior mechanisms face limitations as environmental complexity escalates. This paper explores the potential of applying multi-agent reinforcement learning (MARL) to swarm drone models and proposes strategies to enhance their mission success rates. By utilizing QPLEX and Prioritized Experience Replay (PER), we present methods aimed at improving learning efficiency. Validation through the SMACv2 simulator reveals that the proposed approach achieves faster learning convergence and higher mission success rates compared to existing MARL algorithms.