Deep reinforcement learning for unmanned aerial vehicles cluster task allocation

Yichen Lyu; Wangcheng Zhang; Yaoxin Zhang; Jianhua Ma

doi:10.1117/12.3004002

11 October 2023 Deep reinforcement learning for unmanned aerial vehicles cluster task allocation

Yichen Lyu, Wangcheng Zhang, Yaoxin Zhang, Jianhua Ma

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128006P (2023) https://doi.org/10.1117/12.3004002
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Real-time task allocation for Unmanned Aerial Vehicle (UAV) clusters is a complex decision problem, particularly in dynamic and uncertain environments. In practice, it can be difficult to predict enemy information, which is often obtained gradually through UAV reconnaissance. Traditional algorithms may not perform satisfactorily in such scenarios. In this study, we develop a Deep Reinforcement Learning (DRL) algorithm that combines Proximal Policy Optimization (PPO) with Long Short-Term Memory (LSTM) to address this challenge. By predicting the enemy’s positions and behavior, the algorithm performs real-time task allocation using a pre-trained model. Comparative experiments with basic DRL algorithms validate the convergence, effectiveness, and scalability of our approach.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yichen Lyu, Wangcheng Zhang, Yaoxin Zhang, and Jianhua Ma "Deep reinforcement learning for unmanned aerial vehicles cluster task allocation", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128006P (11 October 2023); https://doi.org/10.1117/12.3004002

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KEYWORDS

Unmanned aerial vehicles

Algorithm development

Education and training

Computer simulations

Weapons

Data modeling

Decision making

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