Hierarchical reinforcement learning (HRL) is a method where complex tasks are broken down into smaller, manageable subtasks organized in a hierarchy. Instead of learning a single policy for an entire problem, HRL uses multiple levels of decision-making. Higher levels handle abstract goals, while lower levels execute specific actions. This approach mimics how humans tackle large problems by dividing them into steps, making it easier for the agent to learn and generalize in environments with long-term dependencies or sparse rewards.
A key idea in HRL is temporal abstraction. For example, a high-level policy might decide to “navigate to a room” in a robotics task, while a low-level policy handles actions like “avoid obstacles” or “turn left.” The high-level policy sets subgoals (e.g., “reach the door”) and delegates them to lower levels, which operate over extended time periods. This reduces the complexity of learning by limiting the scope of each policy. Techniques like options framework or MAXQ decomposition formalize this by defining reusable subtasks. For instance, in a delivery robot, an option could be “pick up an item,” which involves sub-actions like moving to the item and gripping it. Each subtask can be pretrained and reused across different scenarios, improving efficiency.
HRL offers practical benefits. First, it speeds up training by reducing the number of decisions the agent needs to explore. For example, a game AI using HRL might have a high-level strategy to “secure resources” and low-level policies to “mine ore” or “build units.” Second, it improves transfer learning: subtasks like “object avoidance” can be reused in different tasks. However, challenges include designing the hierarchy (manually or through automation) and ensuring coordination between levels. Libraries like RLlib support HRL implementations, enabling developers to experiment with hierarchical structures in custom environments.