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
SATech-01 is an experimental satellite for space science exploration and on-orbit demonstration of advanced technologies.The satellite is equipped with 16 experimental payloads and supports multiple working modes to m...SATech-01 is an experimental satellite for space science exploration and on-orbit demonstration of advanced technologies.The satellite is equipped with 16 experimental payloads and supports multiple working modes to meet the observation requirements of various payloads.Due to the limitation of platform power supply and data storage systems,proposing reasonable mission planning schemes to improve scientific revenue of the payloads becomes a critical issue.In this article,we formulate the integrated task scheduling of SATech-01 as a multi-objective optimization problem and propose a novel Fair Integrated Scheduling with Proximal Policy Optimization(FIS-PPO)algorithm to solve it.We use multiple decision heads to generate decisions for each task and design the action mask to ensure the schedule meeting the platform constraints.Experimental results show that FIS-PPO could push the capability of the platform to the limit and improve the overall observation efficiency by 31.5%compared to rule-based plans currently used.Moreover,fairness is considered in the reward design and our method achieves much better performance in terms of equal task opportunities.Because of its low computational complexity,our task scheduling algorithm has the potential to be directly deployed on board for real-time task scheduling in future space projects.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 Strategic Priority Program on Space Science,Chinese Academy of Sciences。
文摘SATech-01 is an experimental satellite for space science exploration and on-orbit demonstration of advanced technologies.The satellite is equipped with 16 experimental payloads and supports multiple working modes to meet the observation requirements of various payloads.Due to the limitation of platform power supply and data storage systems,proposing reasonable mission planning schemes to improve scientific revenue of the payloads becomes a critical issue.In this article,we formulate the integrated task scheduling of SATech-01 as a multi-objective optimization problem and propose a novel Fair Integrated Scheduling with Proximal Policy Optimization(FIS-PPO)algorithm to solve it.We use multiple decision heads to generate decisions for each task and design the action mask to ensure the schedule meeting the platform constraints.Experimental results show that FIS-PPO could push the capability of the platform to the limit and improve the overall observation efficiency by 31.5%compared to rule-based plans currently used.Moreover,fairness is considered in the reward design and our method achieves much better performance in terms of equal task opportunities.Because of its low computational complexity,our task scheduling algorithm has the potential to be directly deployed on board for real-time task scheduling in future space projects.
