1. A computer implemented method for generating a storage policy for a storage system based on simulation results associated with a state of the storage system, the method comprising: receiving a target function applicable to a storage system, wherein the target function represents a measure of values associated with storage parameters related to productivity and loss tolerance of the storage system; wherein the simulation results for a state of the storage system are analyzed based on (a) the storage system's response to a set of simulated file-related storage operation requests generated based on one or more simulation rules implemented based on a storage target function, and (b) the storage system's response to the set of simulated file-related storage operation requests had the one or more simulation rules not been implemented, such that the one or more simulation rules are implemented based on one or more storage policies generated by way a machine learning entity; generating one or more storage policies, by the machine learning entity, in response to analyzing the simulation results applying reinforced learning; and selecting at least one storage policy that provides a desired result defined by the target function.