Task scheduling network training method based on deep reinforcement learning

A non-transitory readable storage medium with computer programs stored thereon, wherein when the computer instructions are executed by a processor, the processor is configured to perform the following operations:

• acquiring a job to be executed, wherein the job to be executed comprises a plurality of tasks;
• scheduling the plurality of tasks by using a task scheduling network;
• determining rewards at a plurality of time steps according to a scheduling result of the task scheduling network, wherein respective reward at each of the time steps comprises a carbon reward, an electricity cost reward and a task latency reward, the carbon reward represents a carbon emission cost of a data center at the time step, the electricity cost reward represents an electricity cost of the data center at the time step, the task latency reward represents a task latency of the data center at the time step;
• calculating a return Gt according to the rewards at the plurality of time steps, wherein the return Gt is a cumulative reward from a time step t to the end of a scheduling process;
• and
• adjusting parameters of the task scheduling network based on a gradient algorithm according to at least the return Gt.

A terminal device, comprising a memory and a processor, wherein the memory has computer instructions stored thereon, when the computer instructions are executed by the processor, the processor is configured to:

• acquire a job to be executed, wherein the job to be executed comprises a plurality of tasks;
• schedule the plurality of tasks by using a task scheduling network;
• determine rewards at a plurality of time steps according to a scheduling result of the task scheduling network, wherein respective reward at each of the time steps comprises a carbon reward, an electricity cost reward and a task latency reward, the carbon reward represents a carbon emission cost of a data center at the time step, the electricity cost reward represents an electricity cost of the data center at the time step, the task latency reward represents a task latency of the data center at the time step;
• calculate a return Gt according to the rewards at the plurality of time steps, wherein the return Gt is a cumulative reward from a time step t to the end of a scheduling process;
• and
• adjust parameters of the task scheduling network based on a gradient algorithm according to at least the return Gt.