Learning a hierarchy

Hierarchical reinforcement learning mirrors how humans combine learned subskills to solve complex tasks.

Instead of searching over every low-level action, it represents behavior as shorter sequences of high-level actions.

This action abstraction can make long-horizon tasks far more sample-efficient and scalable.

The approach aims to reduce the inefficiency of brute-force reinforcement learning on problems with thousands of steps.