Augmented reality multi-agent cooperative confrontation implementation method based on reinforcement learning

An augmented reality and reinforcement learning technology, applied in the field of information, can solve the problems of single multi-agent behavior strategy, poor cooperative intelligence, poor interaction experience between virtual and real targets, etc., and achieve the effect of flexible and changeable cooperative confrontation strategies.

Inactive Publication Date: 2021-09-24
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0005] In view of this, the purpose of the present invention is to provide an augmented reality multi-agent cooperative confrontation implementation method based on reinforcement learning, which can solve the problems caused by single multi-agent behavior strategy and poor cooperative intelligence in the augmented reality simulation confrontation environment. The problem of poor interactive experience of virtual and real targets

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  • Augmented reality multi-agent cooperative confrontation implementation method based on reinforcement learning
  • Augmented reality multi-agent cooperative confrontation implementation method based on reinforcement learning
  • Augmented reality multi-agent cooperative confrontation implementation method based on reinforcement learning

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Embodiment

[0051] A method for realizing multi-agent cooperative confrontation in augmented reality based on reinforcement learning, the specific steps of which include:

[0052] Step 1: Build the map offline. The FARO laser scanner is used to construct a 3D dense point cloud map model of the real scene offline. When scanning the experimental scene, multi-site scanning is adopted, and the location of each site is Figure three The three-dimensional data is converted to the coordinate system with the origin of the first scanning site through coordinate system transformation. After scanning, use the RGBD panorama of 3D points and color information provided by the scanning results of the FARO laser scanner, and convert the panorama into a triangular grid map through the greedy projection triangulation algorithm. The final offline map model is as follows: figure 1 shown.

[0053] Step 2: Import the constructed real scene map model into the Unity3D 3D rendering engine, and use the bounding...

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Abstract

The invention discloses a multi-agent confrontation simulation environment implementation method in the augmented reality environment, a deep reinforcement learning network is combined with curriculum learning to predict behaviors of all agents and make decisions, and then a reinforcement learning agent model which completes training is migrated to the augmented reality environment. The problem of poor man-machine interaction experience caused by a single virtual multi-agent cooperation strategy in an augmented reality confrontation simulation environment can be solved, and the effect of enabling the cooperation confrontation strategy between a real user and the virtual multi-agent to be flexible and changeable is achieved.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to an augmented reality multi-agent cooperative confrontation realization method based on reinforcement learning. Background technique [0002] In recent years, with the continuous breakthrough of artificial intelligence (Artificial Intelligence, hereinafter referred to as "AI") related technologies and the continuous maturity of related algorithms, AI agents have gradually penetrated into various fields, and are used in intelligent robots, unmanned vehicles, virtual reality and Fields such as augmented reality have shown good application results. In the augmented reality simulation confrontation environment, a good virtual-real interactive experience has become an important link that needs to be optimized, and the intelligence of virtual targets is one of the keys to improving the virtual-real interactive experience. In the current augmented reality simulation con...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06T17/20G06T15/00
CPCG06N3/004G06T17/20G06T15/005G06T2200/04
Inventor 陈靖张君瑞周俊研
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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