AI engineering glossary · Reinforcement learning
What is HRL (hierarchical reinforcement learning)?
Hierarchical reinforcement learning (also called HRL): Approaches that split a long task into layers, with a high-level rule choosing sub-goals or skills and lower-level rules carrying them out. Temporal abstraction shortens the effective horizon and helps with sparse rewards.
Hierarchical reinforcement learning: an example
A cleaning robot's top layer picks 'go to kitchen'; the lower layer handles wheel commands.
Related terms
More Reinforcement learning terms
- Hindsight experience replay
- Horizon
- Imitation learning
- Importance sampling ratio
- Intrinsic motivation
- Inverse reinforcement learning
- Markov decision process
- Maximum entropy reinforcement learning
Where you learn it
Hierarchical reinforcement learning is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.