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Multi-UAV Cooperative Target Assignment Method Based on Reinforcement Learning
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DOI：10.3390/drones8100562.png)
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To overcome the problems of traditional distributed target allocation algorithms in terms of lack of target strategic priority, poor scalability, and robustness, this paper proposes a proximal strategy optimization algorithm that combines threat assessment and attention mechanism (TAPPO). Based on the distributed training framework, the algorithm integrates a threat assessment and dynamic attention strategy and designs a dynamic reward function based on the current hit rate of the drone and the missile benefit ratio to improve the algorithm's exploration ability and scalability. Through an 8vs8 multi-UAV confrontation experiment in a digital twin simulation environment, the results show that the agent using the TAPPO algorithm for target allocation defeats the state machine with an 85% winning rate and is significantly better than other current mainstream target allocation algorithms, verifying the effectiveness of the algorithm.
Keyword:
target assignment
multi-UAV air combat
reinforcement learning
attention mechanism
PPO
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4.8
论文数:
3.9K
被引数:
8.3K
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