Multi-machine collaborative air combat planning method and system based on deep reinforcement learning

Abstract

According to the multi-aircraft cooperative air combat planning method and system based on deep reinforcement learning provided by the invention, a combat aircraft is regarded as an intelligent agent, a reinforcement learning agent model is constructed, and a network model is trained through a centralized training-distributed execution architecture, so that the defect that the exploratory performance of a network model is not strong due to low action distinction degree among different entities during multi-aircraft cooperation is overcome; and by embedding expert experience in the reward value, the problem that a large amount of expert experience support is needed in the prior art is solved. Through an experience sharing mechanism, all agents share one set of network parameters and experience playback library, and the problem that the strategy of a single intelligent agent is not only dependent on the feedback of the own strategy and the environment, but also influenced by the behaviors and cooperation relationships of other agents is solved. By increasing the sampling probability of the samples with large absolute values of the advantage values, the samples with extremely large or extremely small reward values can influence the training of the neural network, and the convergence speed of the algorithm is accelerated. The exploration capability of the intelligent agent is improved by adding the strategy entropy.

Description

technical field [0001] The invention belongs to the technical field of space cooperative combat, and in particular relates to a multi-machine cooperative air combat planning method and system based on deep reinforcement learning. Background technique [0002] Since the 1990s, the development of information technology has promoted military reform. The traditional combat style in which each platform uses its own sensors and weapon systems to detect, track, and strike targets can no longer meet the needs of digital warfare. Facing the increasingly complex battlefield environment in modern warfare, a single fighter has limited ability to detect, track and attack targets, and cannot independently complete the designated air-to-air combat missions. Therefore, multiple fighters need to cooperate to maximize combat effectiveness. [0003] Multi-aircraft coordinated air combat refers to a way of warfare in which two or more combat aircraft cooperate and cooperate with each other to ...

AI Technical Summary

Problems solved by technology

[0014] The purpose of the present invention is to provide a multi-aircraft cooperative air combat planning method and system based on deep reinforcement learning, so as to solve the problem of multi-aircraft cooperative air combat planning in the prior art in the real air combat scenario where incomplete information is confronted, the opponent's strategy is unknown and changes in real time. When the system makes air combat decisions, the amount of calculation is large, the solution is difficult, and it cannot meet the technical problems of real-time decision-making

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