Digital twin-based edge-end collaborative scheduling method for heterogeneous tasks and resources

A digital twin-based edge-end collaborative scheduling method for heterogeneous tasks and resources, characterized by achieving collaborative scheduling of heterogeneous tasks and heterogeneous computation and communication resources based on multi-agent deep reinforcement learning, and comprising the following steps:

• 1) establishing an edge wireless network based on digital twin;
• 2) constructing an edge-end collaborative scheduling problem of heterogeneous tasks and resources according to the deadline requirements of the heterogeneous tasks and the constraints of the heterogeneous computation and communication resources;
• 3) converting the scheduling problem into a multi-agent Markov decision process problem;
• 4) constructing an Actor-Critic neural network model based on the multi-agent deep reinforcement learning to solve the multi-agent Markov decision process problem;
• 5) performing offline centralized training of the Actor-Critic neural network model by digital twin to obtain an experience pool and neural network parameters;
• 6) perceiving an environment state online by end devices, and performing distributed execution of task offloading and computation and communication resource according to the Actor-Critic neural network model under centralized training to collaboratively process the heterogeneous tasks and minimize the total task processing delay.