HRL — Hierarchical Reinforcement Learning

Fact

HRL breaks long reinforcement learning jobs into higher goals and lower skills.

In Plain Words

HRL is like cleaning your room with a tiny boss in your head. The boss says “make it livable,” then sends smaller helpers for socks and scary cups.

Robots and game AIs use it for long jobs. It cuts trial and error by reusing small learned skills.

Related Concepts

• Reinforcement Learning
• Options Framework
• Markov Decision Process
• Long-horizon task

RL
HRL is a layered way to use RL on long tasks.

Options Framework
The Options Framework gives HRL reusable sub-policies.

MDP
HRL usually uses an MDP for states, actions, and rewards.

Long-horizon
HRL breaks long tasks into levels, so they are easier to explore.