This paper presents a \emph{behavioral learning framework} for tuning proportional–integral–derivative (PID) controllers using only closed-loop measurements, without identification, surrogate modeling, or frequency–response analysis. The method evaluates the closed-loop behavior induced by a candidate set of PID gains through an empirical cost functional capturing steady-state accuracy, overshoot, oscillation magnitude, and control effort. At each iteration, the controller is updated toward the gain vector that minimizes the measured behavioral cost within a stability-certified candidate set, guaranteeing \emph{monotonic performance improvement} and bounded adaptive updates. The update rule admits an implicit gradient interpretation and constitutes a model-free generalization of classical adaptive tuning principles. Six benchmark studies—covering first-order, second-order, dead-time, nonlinear, saturating, disturbance-driven, and time-varying plants—demonstrate rapid convergence, robustness, and superior closed-loop behavior compared with classical PID tunings. The proposed approach offers a practical and interpretable alternative to model-based or heuristic PID tuning for uncertain and nonlinear systems.