Hierarchical Reinforcement Learning (HRL) is a research area in machine learning that extends standard reinforcement learning by decomposing complex tasks into hierarchically organized sub-tasks or options, enabling more efficient exploration, faster learning, and improved policy generalization. Foundational studies introduced frameworks such as the options framework, MAXQ value function decomposition, and feudal reinforcement learning, which allow agents to learn at multiple temporal and abstraction levels. Recent research integrates deep learning techniques to model high-dimensional state and action spaces, employing deep HRL architectures, subgoal discovery, and intrinsic motivation to automatically identify useful sub-tasks. HRL has been successfully applied in robotics for sequential manipulation and locomotion tasks, game playing, autonomous navigation, and multi-agent systems, demonstrating improved learning efficiency and scalability in complex environments. Current works also explore combining HRL with meta-learning, imitation learning, and transfer learning to enhance adaptability across diverse tasks and dynamic environments.