Deep reinforcement learning has shown its effectiveness in various applications, providing a promising direction for solving tasks with high complexity. However, naively applying classical RL complex long-horizon task single control policy is inefficient. Thus, modularization tackles this problem by set of modules that are mapped to primitives and properly orchestrating them. In study, we furth...