Reinforcement-learning modeling interfaces

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:

• transmitting a user interface to be displayed to a user, wherein the user interface comprises one or more first interactive elements, wherein the one or more first interactive elements display policy settings of a reinforcement learning model, and wherein the one or more first interactive elements are configured to allow the user to update the policy settings of the reinforcement learning model;
• receiving one or more inputs from the user, wherein the inputs comprise one or more modifications of at least a portion of the one or more first interactive elements of the user interface to update the policy settings of the reinforcement learning model;
• and
• training a neural network model using the reinforcement learning model with the policy settings as updated by the user to adjust rewards assigned in the reinforcement learning model,
• wherein:
• the neural network model comprises a routing agent model that is trained in portions using respective simulated subsets of training environments at different times;
• and
• the respective simulated subsets of the training environments are generated using hierarchical algorithmic clustering comprising first-order hierarchical clusters and second-order hierarchical clusters.