Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) is a reinforcement learning approach that constructs multi-level decision-making structures to break down complex tasks into multiple sub-tasks, thereby improving learning efficiency and addressing high-dimensional state space problems. HRL aims to optimize long-term rewards, enabling efficient and flexible task execution and environmental adaptation. It is widely applied in areas such as robot navigation, game strategies, and resource management.