Systems, computer program products, and methods are described herein for dynamically determining performance benchmarking parameters based on reinforcement learning. The present invention is configured to implement the first distributed impact simulation model on an application; initiate a reinforcement learning algorithm on the application, wherein initiating further comprises receiving a performance assessment output for the one or more application parameters; initiate an optimization policy generation engine on the performance assessment output associated with the application parameters to generate an optimization to encode the performance assessment output into rewards and costs; initiate an implementation of the optimization policy on the application 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 application parameters; and implement the second distributed impact simulation model on the application.