Definition
Hierarchical AI systems organize information or control into a tree-like structure of nested layers. In Reinforcement Learning, Hierarchical RL decomposes complex tasks into sub-goals managed by higher-level policies, while lower-level policies execute primitive actions. Similarly, in deep learning, hierarchical feature extraction allows early layers to detect simple patterns (edges) and deeper layers to recognize complex objects (faces). This structure improves scalability, interpretability, and sample efficiency by breaking down monolithic problems into manageable components.
Summary
Refers to AI architectures or learning strategies organized into multiple levels of abstraction, where higher levels control lower ones.
Key Concepts
- Abstraction Levels
- Sub-goaling
- Feature Extraction
- Modularity
Use Cases
- Complex robotic task decomposition
- Deep neural network feature learning
- Natural language processing with syntax trees