Abstract: Although reinforcement learning （RL） is an effective approach for building autonomous agents that improve their performance with experiences, a fundamental problem of the standard RL algorithm is that in practice they are not solvable in reasonable time. The hierarchical reinforcement learning （HRL） is a successful solution which decomposes the learning task into simpler subtasks and learns each of them independently. As a promising HRL, option is introduced as closed-loop policies for sequences of actions to enable HRL. A key problem for HRL based on options is to discover the correct subgoals online. Through analyzing the actions of agents in subgoals, two useful properties are found: （1） the subgoals have more possibility to be passed through and （2） the effective actions in subgoals are restricted. As a consequence, subgoals can be regarded as the most matching action-restricted states in the paths. Considering the grid environment, the concept of unique-direction value is proposed to denote the action-restricted property, and the option discovering algorithm based on unique-direction value is introduced. The experiments show that the options discovered by the unique-direction value method can speed up the primitive Q learning significantly. Moreover, the experiments further analyze how the size and generating time of options affects the performance of Q learning.