Multi-agent reinforcement learning method for collaborative decision-making of multiple combat units

Abstract

A multi-agent reinforcement learning method for collaborative decision-making of multiple combat units comprises the following steps that a multi-agent reinforcement learning model is established for a red-blue square game confrontation scene, and intelligent collaborative decision-making modeling for the multiple combat units is achieved; the number of effective training samples is increased by adopting a post-event target conversion method, and optimization convergence of a multi-agent reinforcement learning model is achieved; constructing a reward function by taking a team global task reward as a benchmark and taking a specific action reward of each combat unit as feedback information; and generating a plurality of opponent strategies according to different combat schemes, and training the multi-agent reinforcement learning model through massive simulation game confrontation by using a reward function. The method solves the problems that in the prior art, red and blue square game confrontation multi-combat-unit decision collaboration is low, and valuable training samples are difficult to obtain.

Description

technical field [0001] The invention belongs to the field of artificial intelligence technology game confrontation, and relates to a multi-agent enhanced learning method. Background technique [0002] Multi-agent deep reinforcement learning combines the collaborative ability of multi-agents with the decision-making ability of reinforcement learning to solve the collaborative decision-making problem of clusters and multi-units. It is an emerging research hotspot and application direction in the field of machine learning, which covers many algorithms. , rules, and frameworks, and are widely used in real-world fields such as automatic driving, energy distribution, formation control, track planning, routing planning, and social problems. It has extremely high research value and significance. Relevant foreign research institutions have carried out some preliminary basic technology research on multi-agent deep reinforcement learning, and domestic research on this technology, esp...

AI Technical Summary

Problems solved by technology

[0004] The technical problem of the present invention is: to overcome the deficiencies of the prior art, to propose a multi-agent enhanced learning method for multi-combat unit collaborative decision-making, and to solve the red-blue party game against multi-combat unit decision-making coordination existing in the prior art Low performance, difficult to obtain valuable training samples, etc.

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