Autonomous Decision-making and Intelligent Collaboration of UAV Swarms Based on Reinforcement Learning with Sparse Rewards

doi:10.12382/bgxb.2022.0177

Abstract

Abstract:

UAV swarms will profoundly shape the pattern of warfare. In order to improve the autonomous decision-making algorithm capability of UAV swarms, the autonomous decision-making method for heterogeneous UAV swarm attack-defense confrontation scenarios is studied. An overview of the design of the UAV swarm confrontation model and the model design of the UAV swarm attack-defense confrontation scenario are carried out. To solve the sparse reward problem which widely exists in the reinforcement learning technology in the autonomous decision-making of the UAV swarm, a reward mechanism setting method based on local reward reshaping is proposed. And then, the prioritized experience replay is superimposed, which effectively improves the sparse reward problem. Finally, the superiority of this method is verified by simulation and demonstration system design. This study will accelerate the network convergence process of the autonomous decision-making algorithm for UAV swarms based on reinforcement learning technology, which is of great significance to the research on autonomous decision-making algorithms of UAV swarms.

Key words: multiple agents, UAV intelligence, game confrontation, reinforcement learning, sparse reward

LI Chao, WANG Ruixing, HUANG Jianzhong, JIANG Feilong, WEI Xuemei, SUN Yanxin. Autonomous Decision-making and Intelligent Collaboration of UAV Swarms Based on Reinforcement Learning with Sparse Rewards[J]. Acta Armamentarii, 2023, 44(6): 1537-1546.

Figures/Tables 12

Fig.1 Schematic diagram of multi-agent reinforcement learning

Fig.2 Composition of the UAV swarm confrontation model

Fig.3 Initial site map of attack-defense confrontation simulation model of the red and blue UAV swarms

Fig.4 Schematic diagram of the autonomous decision-making principle for the redintelligent units

Fig.5 Schematic diagram of the game confrontation strategy of the blueintelligent units

Fig.6 Reward engineering setting based on local reward reshaping in UAV swarm attack-defense confrontation scenarios

Fig.7 Reward sparsity under the local reward reshaping

Fig.8 Framework for autonomous decision-making and intelligent collaboration strategy learning method for UAV swarm confrontation based on local reward reshaping and prioritized experience replay

Fig.9 Efficiency comparison for attack-defense confrontation algorithms of UAV swarms

Table 1 Efficiency comparison forattack-defense confrontation algorithms of UAV swarms

算法	算法效果	性能提升
DQN +局部回报重塑算法	训练2000代,策略收敛,胜率约80%。
Double DQN+局部回报重塑算法	训练1500代,策略收敛,胜率约80%。	提升25%
Double DQN+局部回报重塑+PER算法	训练700代,策略收敛,胜率约80%。	提升65%

Fig.10 Situation forattack-defense confrontation simulation of red and blue UVA swarms

Fig.11 Demonstration panel for attack-defense confrontation scenario of UVA swarms

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