A Mimic Environment and Battlefield Situation Strategy Transfer Technology Based on Adversarial Discrimination Transfer Method

A discriminative and environmental technology, applied in the field of artificial intelligence, can solve problems such as high cost and reduce the cost of dependence

Active Publication Date: 2021-11-05
南京星耀智能科技有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In some environment-based training models, when the learning model is applied to a real scene, if the scene is different from the scene configured for training, most current systems are very fragile
Some research methods use real environment data for model training, but the model collects real scene data in the real environment, which is very expensive

Method used

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  • A Mimic Environment and Battlefield Situation Strategy Transfer Technology Based on Adversarial Discrimination Transfer Method
  • A Mimic Environment and Battlefield Situation Strategy Transfer Technology Based on Adversarial Discrimination Transfer Method
  • A Mimic Environment and Battlefield Situation Strategy Transfer Technology Based on Adversarial Discrimination Transfer Method

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Embodiment Construction

[0019] The following will further describe the accompanying drawings of the present invention in conjunction with the embodiments.

[0020] By obtaining real combat drill information from some military databases, it can simulate the tactical drill scene training in the real world. In this part, a CNN-based policy representation framework is introduced, and guided policy search (mapping the attributes of weapons and equipment, real information of the environment, etc. into the data matrix) is added, which can reduce the number of training samples in the real world. This method has achieved good results in some complex tasks.

[0021] First of all, based on the DQN network, the modular design is carried out, and the bottleneck structure is used to connect the perception module and the control module. The bottleneck structure can help the neural network learn low-dimensional feature representation. Through the bottleneck structure perception module, it can learn from the origina...

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Abstract

The present invention provides a mimetic environment and battlefield situation strategy transfer technology based on an adversarial discriminative migration method, adopts an adversarial discriminative method for regression migration, and evaluates the effectiveness of tactical strategies from a mimetic environment to a real environment. Randomization, using a real RGB camera to record real combat scenes, such as mountains, rivers, military bases, etc., the strategy learned in the simulation is robust enough to directly transfer the learned strategy to Real combat scenarios; through the method of simulation, the cost of relying on the real world during mimetic migration is greatly reduced.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a mimetic environment and battlefield situation strategy transfer technology based on an adversarial discriminative transfer method. Background technique [0002] At present, in military units, war games are mostly used for turn-based tactical exercises. War game deduction cannot achieve real-time drills in actual combat exercises, and cannot truly simulate combat scenarios. Therefore, when conducting large-scale and long-term battles, war games can only focus on small-scale, short-term combat effects, and cannot achieve long-term tactical transfer. By building a combat mimetic environment, a real mimetic environment can be constructed through various sensors and intelligence data, and real-time tactical strategies can be generated to better respond to dynamic changes in the battlefield. [0003] In some environment-based training models, when the learning mod...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G09B9/00G06N3/08G06N3/04
CPCG09B9/003G06N3/084G06N3/045
Inventor 杨理想张侨王银瑞范鹏炜
Owner 南京星耀智能科技有限公司
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