System for threshold detection using learning reinforcement

A computer program product for dynamic parametric modeling using learning reinforcement, the computer program product comprising a non-transitory computer-readable medium comprising code causing a first apparatus to:

• electronically retrieve a first distributed impact simulation model, wherein the first distributed impact simulation model is generated using a first set of actions;
• implement the first distributed impact simulation model on an application within a distributed environment, wherein implementing further comprises initiating the first set of actions on one or more application parameters;
• initiate a reinforcement learning algorithm on the application, wherein initiating further comprises receiving a performance assessment output for the one or more application parameters based on at least initiating the first set of actions on the one or more application parameters;
• initiate an optimization policy generation engine on the performance assessment output associated with the one or more application parameters to generate an optimization policy, wherein the optimization policy generation engine is configured to encode the performance assessment output into rewards and costs, wherein encoding further comprises assigning a cost to a first portion of the first set of actions and assigning a reward to a second portion of the first set of actions;
• initiate an implementation of the optimization policy on the application, wherein initiating further comprises generating a second set of actions based on at least the optimization policy to maximize an aggregated reward calculated from the second portion of the first set of actions;
• automatically generate a second distributed impact simulation model using the second set of actions to be implemented on the one or more application parameters;
• and
• implement the second distributed impact simulation model on the application, wherein implementing further comprises initiating the second set of actions on the one or more application parameters.

A method for dynamic parametric modeling using learning reinforcement, the method comprising:

• electronically retrieving a first distributed impact simulation model, wherein the first distributed impact simulation model is generated using a first set of actions;
• implementing the first distributed impact simulation model on an application within a distributed environment, wherein implementing further comprises initiating the first set of actions on one or more application parameters;
• initiating a reinforcement learning algorithm on the application, wherein initiating further comprises receiving a performance assessment output for the one or more application parameters based on at least initiating the first set of actions on the one or more application parameters;
• initiating an optimization policy generation engine on the performance assessment output associated with the one or more application parameters to generate an optimization policy, wherein the optimization policy generation engine is configured to encode the performance assessment output into rewards and costs, wherein encoding further comprises assigning a cost to a first portion of the first set of actions and assigning a reward to a second portion of the first set of actions;
• initiating an implementation of the optimization policy on the application, wherein initiating further comprises generating a second set of actions based on at least the optimization policy to maximize an aggregated reward calculated from the second portion of the first set of actions;
• automatically generating a second distributed impact simulation model using the second set of actions to be implemented on the one or more application parameters;
• and
• implementing the second distributed impact simulation model on the application, wherein implementing further comprises initiating the second set of actions on the one or more application parameters.