What Are Hierarchical AI Agents?
Hierarchical AI agents are ai agents that are structured in a hierachy. Higher level agents will oversee lower levels agents within the hierachy. These levels may be different however, based on how comple the system is. Hierarchical AI agents are useful in a variety of applications that includes areas of robotics, transportation systems and manufacturing to name a few. Hierarchical AI agents great for prioritizing multiple task as well as sub tasks under it's hierarchy as well as coordinating these processes in an efficient manner. Think of a hierarchical agent working just like a corporate organization with many levels within the organization.
Hierarchical AI agents organize specific tasks in hierachy that is strutured and consisting of different levels. The higher level agent supervise and can delegate goals into smaller tasks withing the hierachy. Many time, the lower level Hierarchical AI agents will execute these specific tasks and can provide reports on the progress of the task. If the system is complex, Hierarchical AI agents may have intermediate level agents that can coordinate the activities and processes of the lower level agents with higher agents. Pretty cool huh!
There are many advantages of Hierarchical AI agents. These agents offer efficiency with resources because they assign tasks to the most equipped lower level agents, which in turn helps avoid duplicate efforts. Hierarchical AI agents are great at enhancing communication within an organization by utilizing Hierarchical Refinforcement Learning or (HRL) that helps them improve decision making of the agents by reducing the complexity of the agent's actions. This helps enhance agent exploration. Hierarchical AI agents produce high level actions to help simplify a problem and improve learning for all the agents involved in the hierachy.
Since organizations can be vast and complex, the complexity may become an issue when these hieratchies are used for problem solving. Hierarchical AI agents if they are fixed can limity the ability to adapt in certain environments that may be changing. This can hinder adjustments for the Hierarchical AI agent. Many of the hierarchies may lack the ability to be reused across other parts of hte organization that can be time consuming and may require additional expertise. Lastly, labeled training data is needed for a specific algorithmic design. If the desire to apply standard maching learning techniques to help improve performance may be difficult as there are a variety of complexities involved within any given organization.