Multi-aircraft cooperative air combat planning method and system based on deep reinforcement learning

Abstract

The present invention proposes a multi-aircraft cooperative air combat planning method and system based on deep reinforcement learning. By treating fighter aircraft as an agent, a reinforcement learning agent model is constructed, and the network model is trained through a centralized training-distributed execution architecture, which overcomes the In the case of multi-machine collaboration, due to the small degree of distinction between actions between different entities, the network model is not very exploratory. By embedding expert experience in the reward value, the problem of requiring a large amount of expert experience in the prior art is solved. Through the experience sharing mechanism, all agents share a set of network parameters and experience playback library, which solves the problem that the strategy of a single agent not only depends on its own strategy and feedback from the environment, but also is affected by the behavior and cooperation of other agents. By increasing the sampling probability of samples with larger absolute values ​​of advantages, samples with extremely large or extremely small reward values ​​can affect the training of the neural network and speed up the convergence of the algorithm. Improve the agent's exploration ability by adding policy 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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