Multi-agent behavior decision-making method and device, electronic device and storage medium

A technology of multi-agent and decision-making method, which is applied in the fields of electronic equipment and storage media, multi-agent behavior decision-making methods, and devices, can solve problems such as learning instability, achieve collision avoidance effects, realize independent decision-making capabilities, and achieve high The effect of performance

Active Publication Date: 2021-07-16
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

[0009] The invention provides a multi-agent behavior decision-making method, device, electronic equipment...

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  • Multi-agent behavior decision-making method and device, electronic device and storage medium
  • Multi-agent behavior decision-making method and device, electronic device and storage medium
  • Multi-agent behavior decision-making method and device, electronic device and storage medium

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[0055] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0056] figure 1 A schematic flow chart of the multi-agent behavior decision-making method provided by the embodiment of the present invention, such as figure 1 As shown, the method includes:

[0057] Step 110, based on the graph generation module in the multi-agent behavior model, construct each agent and its corresponding environment information as a graph;

[0058] Step 120, based on the...

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Abstract

The invention provides a multi-agent behavior decision method and device, an electronic device and a storage medium, and the method comprises the steps: constructing each agent and corresponding environment information into a graph based on a graph generation module in a multi-agent behavior model; based on an information transmission module in the multi-agent behavior model, encoding each agent and the corresponding environment information to obtain a joint encoding state corresponding to each agent; based on a strategy optimization module in the multi-agent behavior model, determining an initial decision of each agent in combination with the joint coding state corresponding to each agent; and based on a collision avoidance module in the multi-agent behavior model, performing variable step size control on the initial decision of each agent, and determining a final decision of each agent in combination with the repulsive force corresponding to each agent. According to the method, the problem that reinforcement learning is difficult to converge in a large-scale agent scene is solved, and high-performance autonomous decision-making ability and collision avoidance effect in a multi-agent system are realized.

Description

technical field [0001] The invention relates to the technical field of swarm intelligence, in particular to a multi-agent behavior decision-making method, device, electronic equipment and storage medium. Background technique [0002] Research has found that gregarious organisms such as "bee colonies" can emerge macroscopically intelligent behaviors through the coordinated behavior of many scattered individuals, forming complex group intelligence, which is a higher-level performance beyond individual behavior. With the development of emerging technologies such as autonomous control, the use of large-scale (homogeneous / heterogeneous) multi-agent "swarm" system swarm intelligence collaborative behavior has become a new way to solve major needs such as smart security, emergency rescue, and smart logistics. . [0003] The future development of the current "swarm" system can be roughly divided into three directions: [0004] (1) Quantitative development: to achieve a larger scal...

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Application Information

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IPC IPC(8): G06N3/00G06N3/08G06N5/02
CPCG06N3/006G06N3/084G06N5/02
Inventor 刘振周志明吴士广蒲志强丘腾海易建强
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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