GradedAction

GradedAction is a theoretical construct used to describe actions in decision-making processes that carry a graded quality or intensity rather than a binary or discrete choice. It frames actions as carriers of a quantitative grade reflecting desirability, feasibility, cost, or multi-criteria performance. The concept is used across decision theory, planning, reinforcement learning, and robotics to capture nuances that discrete actions alone may miss.

Formally, a graded action associates each candidate action with a real-valued grade in the current state. A policy may select the action with the highest grade, or it may sample actions according to a distribution that depends on the grades. Grades can be scalar or involve a vector of criteria, in which case the selection rule may use scalarization, Pareto comparison, or multi-objective optimization.

GradedAction accommodates continuous or large action spaces by avoiding exhaustive enumeration and relying on grading functions that rank actions by usefulness or feasibility. It supports soft decisions, where higher grades imply higher probability, enabling smoother policies and potentially better exploration in learning algorithms.

Applications include robotics, where actions encode both direction and intensity (speed, torque); autonomous driving, where throttle, braking, and steering are graded; and game AI, where agents choose actions with varying strength or aggression. In planning and optimization, graded actions enable partial progress or incremental commitments when full actions are costly or uncertain.

Related concepts include graded utilities, soft action selection, fuzzy logic, and multi-objective decision making. Limitations include the need to define meaningful grade functions, potential computational overhead, and challenges in calibrating grades to reflect real-world trade-offs.