Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinfor...Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA.The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the algorithm.Finally,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation.The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space.The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA.展开更多
This paper presents a new soliton approach to hyper-distributed hyper-parallel self-organizing dynamic scheduling for task allocations among rational autonomous agents in a multi-agent system (MAS). This approach can ...This paper presents a new soliton approach to hyper-distributed hyper-parallel self-organizing dynamic scheduling for task allocations among rational autonomous agents in a multi-agent system (MAS). This approach can overcome many drawbacks of other mechanisms currently used for coalition formation and cooperation in MAS. The thorny problems, such as overabundant bid, social behaviors, colony intelligence, variable neighbors, and interdepen-dency, can easily be treated by using the proposed approach, whereas they are very difficult for other conventional approaches. The simulation on a distributed transport scheduling sys-tem shows the soliton approach featured by hyper-parallelism, effectiveness, openness, dynamic alignment and adaption.展开更多
基金This research was funded by the Project of the National Natural Science Foundation of China,Grant Number 62106283.
文摘Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA.The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the algorithm.Finally,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation.The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space.The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA.
基金the National Natural Science Foundation of China under grant No. 60073008, the NKBRSF of China under grant No. G1999032707 (973
文摘This paper presents a new soliton approach to hyper-distributed hyper-parallel self-organizing dynamic scheduling for task allocations among rational autonomous agents in a multi-agent system (MAS). This approach can overcome many drawbacks of other mechanisms currently used for coalition formation and cooperation in MAS. The thorny problems, such as overabundant bid, social behaviors, colony intelligence, variable neighbors, and interdepen-dency, can easily be treated by using the proposed approach, whereas they are very difficult for other conventional approaches. The simulation on a distributed transport scheduling sys-tem shows the soliton approach featured by hyper-parallelism, effectiveness, openness, dynamic alignment and adaption.
