This paper deals with the scheduling optimization based on dispatching rules for the efficient operation of the semiconductor factory. The modern semiconductor plant has a large scale and complex structure. It requires a very high computational cost in the design of sophisticated scheduling. These problems make it difficult to apply popular scheduling methods such as mathematical formulation and meta-heuristic, which require a high computational cost, to semiconductor factory. Therefore, almost manufacturers have been designing the schedule using very simple dispatching rules. However, in order to be a more efficient operation, more sophisticated dispatching rules should be designed for the factory. Thus, in this paper, we proposed the reinforcement learning-based algorithms for the design of more effective and sophisticated dispatching rules. As a first study, we proposed the per-machine linear dispatching rule learning approach for different multi-machines using population-based search. As a second study, to achieve higher data efficiency, we proposed per-machine dispatching rule learning approach using policy gradient, in which actors are decentralized, and critic is centralized. As a third study, we proposed a hybrid algorithm that takes both the advantage of a policy gradient method and a population-based search. Experiments showed that the proposed methods have better performance or data efficiency than existing methodologies.