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.