Using reinforcement learning to select a ds processing unit

A user computing device of a dispersed storage network (DSN) comprises:

• memory;
• an interface;
• and
• a processing module operably coupled to the memory and the interface, wherein the processing module is operable to:
• for a data access request, access a plurality of estimated efficiency models of a plurality of dispersed storage (DS) processing units of the DSN, wherein an estimated efficiency model of the plurality of estimated efficiency models includes a list of estimated efficiency probabilities corresponding to a list of data access request types for a DS processing unit of the plurality of DS processing units;
• select one of the DS processing units from the plurality of DS processing units based on the plurality of estimated efficiency models, a type of request of the data access request, and a randomizing factor to produce a selected DS processing unit;
• send, via the interface, the data access request to the selected DS processing unit for execution;
• determine an actual processing efficiency of a processing of the data access request by the selected DS processing unit;
• and
• update the estimated efficiency model of the selected DS processing unit based on the actual processing efficiency.