1. A method for deploying storage system resources with learning of workloads applied to a storage system, comprising the steps of: A. setting state-action fuzzy rules for deviated percentages of characteristic parameters of the storage system from SLAs (Service Level Agreement) of workloads under each scenario of a plurality of scenarios and adjustments of resources, and action-reward fuzzy rules for adjustments of resources and reward values, wherein the scenario is a specific relation between a deviated direction of the characteristic parameters and a change of a corresponding resource; B. generating an experience matrix where entries in each row refer to reward values under a specific state, and entries in each column refer to reward values for an adjustment of at least one resource, wherein the entries in the experience matrix are initialized to zero at an initial state and changes while the reward values are updated, and the specific state is refers to a combination of deviated percentages of at least two different kinds of characteristic parameters; C. collecting current deviated percentages of characteristic parameters from one of the workloads, and providing predicted deviated percentages of characteristic parameters for said workload in a plurality of later time points; D. randomly choosing one scenario of a plurality of scenarios and corresponding state-action fuzzy rules and membership functions of the chosen scenario; E. performing membership functions of the state-action fuzzy rules of the chosen scenario based on the collected deviated percentages of characteristic parameters of said workload, and performing fuzzification, fuzzy inference, and result aggregation—to the result of the membership functions to obtain a first action range; F. defuzzifying the first action range to have an adjusted amount for at least one resource; G. executing the adjusted amount in the storage system for the workload; H. processing fuzzification, fuzzy inference, and result aggregation by inputting the provided predicted percentages of characteristic parameters of said workload to chosen scenario's membership functions of the action-reward fuzzy rules to have a reward range; I. defuzzifying the reward range to have a deviated reward value; J. for the rows of the experience matrix corresponding to the states of the predicted deviated percentages of characteristic parameters, searching for the maximum value in each of the rows; K. accumulating a deviated reward value and chosen values in a previous time point from the step I as an updated reward value and replacing the entry of the experience matrix under the state of the deviated percentages of parameters and action amount of the previous time point—with the updated reward value, wherein the chosen values are the maximum values in each of the rows obtained from the step I of the previous cycle of step C to step K; L. repeating step C to step K until each of the reward value stays the same or varies within a limited interval over time, wherein workloads that are not selected to be processed in step C are further processed in turns while step C to step K are repeated; M. choosing a row in the experience matrix corresponding to observed deviated percentages of characteristic parameters; and N. executing the specific adjustment of the resources corresponding to the maximum value among the entries in the row in the storage system.