Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) is a framework in reinforcement learning that decomposes complex tasks into simpler, manageable subtasks. This approach allows agents to learn and optimize their behavior at multiple levels of abstraction, facilitating more efficient learning and decision-making. HRL typically involves a hierarchy of policies, where higher-level policies dictate the overall strategy, while lower-level policies focus on executing specific actions. Common use cases include robotics, game playing, and any domain where tasks can be broken down into a series of subtasks, enabling faster convergence and improved performance.