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Wargame multi-entity asynchronous collaborative decision-making method and device based on reinforcement learning

A technology of reinforcement learning and collaborative decision-making, applied in neural learning methods, machine learning, biological models, etc., can solve problems such as difficult to effectively solve multi-entity asynchronous cooperation in war chess, inconsistent execution time of basic actions, etc. The effect of high combat efficiency and high final win rate

Active Publication Date: 2022-08-09
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0007] Second, the heterogeneity of multiple entities in wargames leads to the asynchrony of multi-agent collaboration, that is, the execution time of basic actions of different agents is inconsistent.
This asynchrony makes it difficult for existing multi-agent reinforcement learning algorithms to effectively solve the problem of multi-entity asynchronous cooperation in war games

Method used

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  • Wargame multi-entity asynchronous collaborative decision-making method and device based on reinforcement learning
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  • Wargame multi-entity asynchronous collaborative decision-making method and device based on reinforcement learning

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[0045] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

[0046] like figure 1 As shown, a reinforcement learning-based multi-entity asynchronous collaborative decision-making method for war chess provided by this application, in one embodiment, includes the following steps:

[0047] Step 102: Obtain a wargame environment and a multi-entity asynchronous cooperative decision-making problem corresponding to the wargame environment, and perform modeling and analysis on the multi-entity asynchronous cooperative decision-making problem to obtain an initial model.

[0048] Specifically, the wargame environment and the multi-entity...

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Abstract

The invention belongs to the technical field of intelligent decision making, and relates to a war game multi-entity asynchronous collaborative decision making method and device based on reinforcement learning, and the method comprises the steps: obtaining a war game deduction environment and a multi-entity asynchronous collaborative decision making problem, and carrying out the modeling analysis of the multi-entity asynchronous collaborative decision making problem, and obtaining an initial model; according to the initial model, a multi-agent deep reinforcement learning algorithm is adopted to establish an agent network model and a hybrid evaluation network model; training the agent network model and the hybrid evaluation network model to obtain a collaborative decision framework; a loss function of the multi-agent deep reinforcement learning algorithm is reconstructed by setting a weighting operator or optimizing the multi-agent deep reinforcement learning algorithm through multi-step return; using the reconstructed loss function to update the collaborative decision framework; and according to the updated collaborative decision framework, performing decision making on asynchronous collaboration of multiple entities. According to the method, multi-entity asynchronous collaborative decision-making in war game deduction can be realized.

Description

technical field [0001] The present application relates to the technical field of intelligent decision-making, in particular to a multi-entity asynchronous cooperative decision-making method and device for wargames based on reinforcement learning. Background technique [0002] Wargaming is a process of using wargames to simulate war activities. Wargame players use chessboards and pieces that represent the environment and military power to simulate war confrontation based on specific military rules and probability theory principles, and conduct process deduction, evaluation and optimization of combat plans. The wargame team of the National Defense University developed a strategic campaign wargame system, and pointed out the key problem that needs to be solved in the application of artificial intelligence technology to wargames - intelligent situational awareness. The early wargame agent design mainly used the high-level human player's deduction experience to form a knowledge ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/00G06N3/04G06N3/08G06N20/00
CPCG06F30/27G06N3/006G06N3/08G06N20/00G06N3/044
Inventor 张煜蒋超远罗俊仁李婷婷刘运杨景照刘果李鑫刘屹峰陈佳星
Owner NAT UNIV OF DEFENSE TECH
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