The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to...The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation,there is low efficiency in dealing with complex tasks,and there are interactive conflicts in multiagent systems.This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents(OGMN)to reduce task assignment conflicts.Considering the slow speed of traditional dynamic task assignment algorithms,this paper proposes the proximal policy optimization for task assignment of general and narrow agents(PPOTAGNA)algorithm.The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning(DRL)adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency.Finally,simulation experiments are carried out in the digital battlefield.The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio.By analyzing agent behavior,the efficiency,superiority and rationality of resource utilization of this method are verified.展开更多
Low-precision training has emerged as a practical approach,saving the cost of time,memory,and energy during deep neural networks(DNNs)training.Typically,the use of lower precision introduces quantization errors that n...Low-precision training has emerged as a practical approach,saving the cost of time,memory,and energy during deep neural networks(DNNs)training.Typically,the use of lower precision introduces quantization errors that need to be minimized to maintain model performance,often neglecting to consider the potential benefits of reducing training precision.This paper rethinks low-precision training,highlighting the potential benefits of lowering precision:(1)low precision can serve as a form of regularization in DNN training by constraining excessive variance in the model;(2)layer-wise low precision can be seen as an alternative dimension of sparsity,orthogonal to pruning,contributing to improved generalization in DNNs.Based on these analyses,we propose a simple yet powerful technique-DPC(Decreasing Precision with layer Capacity),which directly assigns different bit-widths to model layers,without the need for an exhaustive analysis of the training process or any delicate low-precision criteria.Thorough extensive experiments on five datasets and fourteen models across various applications consistently demonstrate the effectiveness of the proposed DPC technique in saving computational cost(-16.21%--44.37%)while achieving comparable or even superior accuracy(up to+0.68%,+0.21%on average).Furthermore,we offer feature embedding visualizations and conduct further analysis with experiments to investigate the underlying mechanisms behind DPC’s effectiveness,enhancing our understanding of low-precision training.Our source code will be released upon paper acceptance.展开更多
基金the Project of National Natural Science Foundation of China(Grant No.62106283)the Project of National Natural Science Foundation of China(Grant No.72001214)to provide fund for conducting experimentsthe Project of Natural Science Foundation of Shaanxi Province(Grant No.2020JQ-484)。
文摘The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation,there is low efficiency in dealing with complex tasks,and there are interactive conflicts in multiagent systems.This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents(OGMN)to reduce task assignment conflicts.Considering the slow speed of traditional dynamic task assignment algorithms,this paper proposes the proximal policy optimization for task assignment of general and narrow agents(PPOTAGNA)algorithm.The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning(DRL)adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency.Finally,simulation experiments are carried out in the digital battlefield.The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio.By analyzing agent behavior,the efficiency,superiority and rationality of resource utilization of this method are verified.
基金supported by the National Key R&D Program of China(Grant No.2021YFB0301200)the National Natural Science Foundation of China(Grant No.62025208).
文摘Low-precision training has emerged as a practical approach,saving the cost of time,memory,and energy during deep neural networks(DNNs)training.Typically,the use of lower precision introduces quantization errors that need to be minimized to maintain model performance,often neglecting to consider the potential benefits of reducing training precision.This paper rethinks low-precision training,highlighting the potential benefits of lowering precision:(1)low precision can serve as a form of regularization in DNN training by constraining excessive variance in the model;(2)layer-wise low precision can be seen as an alternative dimension of sparsity,orthogonal to pruning,contributing to improved generalization in DNNs.Based on these analyses,we propose a simple yet powerful technique-DPC(Decreasing Precision with layer Capacity),which directly assigns different bit-widths to model layers,without the need for an exhaustive analysis of the training process or any delicate low-precision criteria.Thorough extensive experiments on five datasets and fourteen models across various applications consistently demonstrate the effectiveness of the proposed DPC technique in saving computational cost(-16.21%--44.37%)while achieving comparable or even superior accuracy(up to+0.68%,+0.21%on average).Furthermore,we offer feature embedding visualizations and conduct further analysis with experiments to investigate the underlying mechanisms behind DPC’s effectiveness,enhancing our understanding of low-precision training.Our source code will be released upon paper acceptance.
