macroaction

A macroaction is a high-level action that represents a sequence of simpler, primitive actions bundled together into a single decision step. In planning and control systems, macroactions are used to increase efficiency by reducing the search space or decision horizon, allowing an agent to operate over longer timescales without evaluating every tiny step.

Contexts and use cases include artificial intelligence planning, reinforcement learning, robotics, and human-computer interaction. In AI planning, macroactions (often called macro-operators) enable faster problem solving by encoding commonly occurring substrategies as single actions. In reinforcement learning, similar concepts appear as options or skills, where a policy is executed for a variable duration until a termination condition is met. In robotics or automation, macroactions correspond to mission-level tasks such as “reach location X,” which comprises several motor commands and sensor checks.

Design and implementation considerations include the choice of granularity, preconditions and effects, and the expected duration of the macroaction. Effective macroactions are reusable across similar states, robust to small environmental changes, and compatible with the underlying decision framework (e.g., Markov or semi-Markov processes). They can be predefined by domain experts or learned from data, often through hierarchical or temporal abstraction methods.

Benefits and limitations: macroactions can speed up planning and execution and improve coordination at higher levels, but they may reduce flexibility, introduce coupling to specific environments, and require careful handling to avoid brittle or conflicting sequences.

Examples include a software macro (recorded key sequence), a robotics task sequence (navigate, align, grasp), or a game strategy (build unit A, then move to location B).

See also: macro-operators, options framework, hierarchical reinforcement learning.