TAAFT
Free mode
100% free
Freemium
Free Trial
Create tool
GlossaryMachine LearningHierarchical Reinforcement Learning

Hierarchical Reinforcement Learning

[ˌhaɪəˈrɑːkɪkəl ˌriːɪnˈfɔːsmənt ˈlɜːnɪŋ]
Machine Learning
Last updated: December 9, 2024

Definition

An approach that decomposes complex tasks into hierarchies of simpler subtasks.

Detailed Explanation

HRL structures the learning process into multiple levels of abstraction, allowing agents to learn and operate at different temporal and spatial scales. It can handle complex, long-horizon tasks by breaking them down into manageable subtasks and learning policies at different levels of abstraction.

Use Cases

Robot task planning, game AI strategy, complex system control, automated planning

Related Terms

Layer Normalization

A technique that normalizes the inputs across the features in a layer to stabilize and accelerate neural network training. Unlike batch normalization it operates on a single training example.

Machine Learning

AdaDelta

An extension of AdaGrad that adapts learning rates based on a moving window of gradient updates to prevent learning rate decay.

Machine Learning

Gradient Clipping

A technique that prevents exploding gradients by limiting their magnitude during training. This helps maintain stable training in deep neural networks.

Machine Learning