Abstract: A method for automatically generating scheduling algorithms, including determining a scheduling policy for a plurality of jobs to be executed on a computer system, where the scheduling policy specifies an execution order of a plurality of jobs; using the scheduling policy in a production environment for a period of time, and collecting data indicative of a business impact of each job executed during the period of time; generating a list of all pairwise comparisons of business impact between the plurality of jobs, together with outcomes of the comparisons; marking each pair for which the comparison outcome is inconsistent with the relative execution order of the pair of jobs according to a predefined criteria to create a reinforcement learning batch; and using the reinforcement learning batch to adjust a decision criteria used to determine the scheduling policy.