We introduce a new method for hierarchical reinforcement learning. Highlevel policies automatically discover subgoals; low-level policies learn to specialize on different subgoals. Subgoals are represented as desired abstract observations which cluster raw input data. High-level value functions cover the state space at a coarse level; low-level value functions cover only parts of the state space at a fine-grained level. Experiments show that this method outperforms several flat reinforcement learning methods in a deterministic task and in a stochastic task.