High-Performance Computing
Machine Learning
An approach that decomposes complex tasks into hierarchies of simpler subtasks.
Detailed Explanation
Hierarchical Reinforcement Learning (HRL) is a method that breaks down complex problems into simpler, manageable subtasks arranged in a hierarchy. This approach enables agents to learn more efficiently by reusing policies for common subtasks and guiding decision-making at different levels of abstraction, ultimately improving learning speed and scalability in complex environments.
Use Cases
- •Use case: Training a robot to navigate nested environments by learning high-level strategies and low-level movements efficiently.
Related Terms
Other terms in the Machine Learning category