We present ASD (Action, Sequence, and Divide), a new framework for Hierarchical Reinforcement Learning (HRL). Present HRL methods construct the task hierarchies but fail to avoid exploration when tasks are to be performed in a particular sequence, resulting in the agent needlessly exploring all permutations of the tasks. When the task hierarchies are used as an ASD framework, the RL agent encounters better constraints, preventing it from pursuing policies that are not valid, thus enabling the agent to achieve the optimal policy faster. The hierarchies created using the methods explained in this paper can be used to solve new episodes of the same environment, as well as similar instances of the problem. The hierarchies generated with an ASD framework can be used to establish an ordering of tasks. The objective is to not only to complete the tasks but also give the agent insights into the sequence of tasks that need to be performed in order to correctly solve a problem. We present an algorithm to generate the hierarchies as an ASD framework. The algorithm has been evaluated on some of the standard RL domains, namely, Taxi and Wargus, and is found to give correct results.