Intelligent agent control method and device based on reinforcement learning

A technology of reinforcement learning and control methods, applied in neural learning methods, machine learning, biological neural network models, etc., can solve problems such as limited algorithm effects, and achieve the effect of simplifying the strategy learning process and facilitating the expansion of quantity and types

Pending Publication Date: 2021-01-12
天津(滨海)人工智能军民融合创新中心
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Problems solved by technology

However, in a large-scale multi-agent environment, especially in an environm

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  • Intelligent agent control method and device based on reinforcement learning
  • Intelligent agent control method and device based on reinforcement learning
  • Intelligent agent control method and device based on reinforcement learning

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[0054] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

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

[0056] In the field of multi-agent cooperative control, in order to effectively simplify the strategy learning process and reduce the complexity of the interaction between agents, the p...

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Abstract

The invention relates to an intelligent agent control method and device based on reinforcement learning. The method comprises the steps of obtaining current local observation of an intelligent agent;taking the current local observation of the intelligent agent as the input of a reinforcement learning model, and obtaining the current execution action of the intelligent agent output by the reinforcement learning model; controlling an intelligent agent to execute the current execution action of the intelligent agent; according to the technical scheme provided by the invention, the strategy learning process in a large-scale multi-intelligent system can be effectively simplified, the number and types of intelligent agents are easy to expand, and the method has potential value in large-scale real world application.

Description

technical field [0001] The invention relates to the field of multi-agent cooperative control, in particular to an agent control method and device based on reinforcement learning. Background technique [0002] In recent years, the rapid development of deep reinforcement learning has made researchers interested in multi-agent reinforcement learning, hoping that it can solve complex and large-scale problems, such as vehicle autonomous driving, resource allocation, swarm robots, human machine interaction, etc. At present, certain research results have been achieved in multi-agent reinforcement learning, such as communication and natural language processing, multi-agent games, traffic control, social dilemmas, etc. At the same time, more and more researchers began to pay attention to the research of large-scale multi-agent reinforcement learning. In large-scale multi-agent systems, a large number of agents with different goals and complex interactions between agents pose great ...

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Application Information

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IPC IPC(8): G06N3/08G06N3/04G06N20/00
CPCG06N3/084G06N3/049G06N20/00G06N3/045
Inventor 史殿习姜浩薛超康颖金松昌郝锋秦伟
Owner 天津(滨海)人工智能军民融合创新中心
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