This invention relates to a preemptive scheduling method for heterogeneous multi-core processors based on multi-agent
reinforcement learning. It includes: constructing a
system model comprising a set of sporadic real-time tasks and a heterogeneous multi-core platform; pre-setting fixed
preemption points for tasks; dividing task execution into non-preemptive segments; modeling the scheduling problem as a
partially observable Markov decision process; designing state representations that include task dynamic attributes and platform heterogeneity characteristics; wherein the task dynamic attributes are normalized values of relative deadline pressure, execution progress ratio, and relaxation; and the platform heterogeneity characteristics include a weighted sum of core computing
power level and global idle computing power; clustering processors according to computing power; assigning independent agents to each cluster; and adopting a fully cooperative mode to share global rewards; employing a centralized training and distributed execution framework; using the discrete soft actor-critic
algorithm during training; with each agent's policy network collaboratively optimizing under the guidance of a centralized value function; and during execution, each agent independently deciding on
preemption timing based on local observations. This invention achieves cooperative preemptive scheduling among heterogeneous cores through multi-agent
reinforcement learning, effectively reducing task overdue rates and
system overhead, and improving real-time performance.