To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method ba...To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning(DRL) algorithm: the multistep double deep Q-network(MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.展开更多
Ⅰ.EXPANSIONThe establishment of securities market in China has been considered one of its important and controversial economic reform policies.Billions of dollars of treasury bills,corporate bonds and shares have bee...Ⅰ.EXPANSIONThe establishment of securities market in China has been considered one of its important and controversial economic reform policies.Billions of dollars of treasury bills,corporate bonds and shares have been issued and traded all over the nation.Securities exchanges were set up in Shanghai and Shenzhen by the end of 1990 and in early 1991.Since then,securities market in China has undergone rapid expansion.展开更多
基金supported by the National Natural Science Foundation of China (No. 61573286)the Aeronautical Science Foundation of China (No. 20180753006)+2 种基金the Fundamental Research Funds for the Central Universities (3102019ZDHKY07)the Natural Science Foundation of Shaanxi Province (2019JM-163, 2020JQ-218)the Shaanxi Province Key Laboratory of Flight Control and Simulation Technology。
文摘To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning(DRL) algorithm: the multistep double deep Q-network(MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.
文摘Ⅰ.EXPANSIONThe establishment of securities market in China has been considered one of its important and controversial economic reform policies.Billions of dollars of treasury bills,corporate bonds and shares have been issued and traded all over the nation.Securities exchanges were set up in Shanghai and Shenzhen by the end of 1990 and in early 1991.Since then,securities market in China has undergone rapid expansion.
