Systems and methods for staffing master controller

A system, comprising:

• one or more processors;
• and
• a memory that stores machine-readable instructions that when executed by the one or more processors cause the system to:
• receive a request to generate an instruction for a next task in a real-life branch environment;
• access a first dataset including attributes of a stored simulated branch environment similar to a second dataset of attributes of the real-life branch environment;
• based at least in part on the first dataset and the second dataset, request or cause generation of a next action or sequence of actions for a real-life worker by a master staffing engine, wherein the master staffing engine is trained on policies and instructions learned from a branch simulation engine that predicts a plurality of simulated branch environments similar to the real-life branch environment, wherein the master staffing engine comprises a master deep reinforcement learning (DRL) agent that includes at least a first neural network (NN) and a second neural network (NN) and wherein the first NN is coupled to receive an input from the second NN and wherein the second NN is a classical NN that computer a real-life reward and a probability of a long-term real-life reward from the next action in the real-life branch environment;
• generate, for transmission to a computing device used by the real-life worker, a data structure that causes the computer device to render a dynamically updated work queue on a display, wherein the work queue comprises an ordered list of potential real-life actions, and wherein a priority of each of the potential real-life actions within the ordered list is determined based on the computed real-life reward or probability of the long-term real-life reward.

A method, comprising:

• receiving a request from a real-life worker or branch to generate by a master staffing engine, an instruction for a next task in a real-life branch environment;
• loading into the master staffing engine, a first dataset including attributes of a stored simulated branch environment similar to a second dataset of attributes of the real-life branch environment;
• based at least in part on the first dataset and the second dataset, causing generation by the master staffing engine, of a next action or sequence of actions for the real-life worker, wherein the master staffing engine is trained on policies and instructions learned from a branch simulation engine that predicts a plurality of simulated branch environments similar to the real-life branch environment, wherein the master staffing engine comprises a master DRL agent that includes at least a first neural network (NN) and a second neural network (NN) and wherein the first NN is coupled to receive an input from the second NN and wherein the second NN is a classical NN that computer a real-life reward and a probability of a long-term real-life reward from the next action in the real-life branch environment;
• and
• generating an output configured to cause a computing device to dispatch a digital work assignment corresponding to a selected real-life action determined based on the computer probability of the real-life reward or long-term real-life reward.