Digital labor system closed-loop regulation and control method based on multi-agent reinforcement learning

By employing a multi-agent reinforcement learning approach, we can perceive and regulate multi-source data in a digital workforce system in real time, construct a multi-party Markov game model, and generate stability-oriented regulation strategies. This approach solves the problems of disconnect between perception and decision-making and the evolution of group behavior, thereby improving the stability and robustness of the system.

CN121961139APending Publication Date: 2026-05-01QINGDAO UNIV OF TECH
0 Cites 0 Cited by

Patent Information

Application Number
CN202610092072.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-23
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Existing multi-agent reinforcement learning frameworks in digital workforce systems suffer from problems such as a disconnect between perception and decision-making, insufficient modeling of group game theory, and a lack of stability objectives. They are unable to effectively cope with changes in individual worker behavior and the evolution of group behavior, thus affecting system stability and robustness.

Method used

By employing a multi-agent reinforcement learning approach, workers, platform providers, and regulatory stakeholders are modeled as multiple interacting agents. Through multi-source data perception and state construction, multi-agent game modeling, reinforcement learning decision generation, and stability assessment, a closed-loop control mechanism is formed to achieve real-time perception, prediction, and control of multi-party game behavior.

Benefits of technology

It enhances the overall stability and robustness of the digital workforce system, and through real-time data perception and strategy adjustment, it strengthens the ability to model and predict group collaborative or adversarial behaviors, ensuring the stable operation of the system in dynamic environments.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the cross technical field of artificial intelligence and digital platform governance, and discloses a digital labor system closed-loop regulation and control method based on multi-agent reinforcement learning, which comprises the following steps: S1, collecting multi-source data in real time and fusing, and constructing a system state vector; s2, modeling key participants of the system into four types of intelligent agents including a platform, a worker, a demand side and a constraint, and constructing a multi-party Markov game model; s3, a centralized reviewer and distributed actuator architecture is adopted, and a combined regulation and control strategy with stability as a target is generated; s4, calculating three types of stability indexes of the laborer, the demand side and the whole system, and mapping the three types of stability indexes into reinforcement learning feedback signals; and S5, mapping the strategy into a platform executable parameter, performing execution after security verification, and feeding back an execution result to the sensing layer to form a closed-loop regulation and control mechanism. According to the method, multi-agent reinforcement learning is adopted to model a multi-party game, real-time sensing, prediction and regulation are realized, and the stability and robustness of a labor system are improved.
Need to check novelty before this filing date? Find Prior Art