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In this paper, a data-driven control approach is de-veloped by reinforcement leing (RL) to solve the global robust optimal output regulation problem (GROORP) of partially linear systems with both static uncertainties and nonlinear dynamic un-certainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncer-tainties. The closed-loop system is ensured to be input-to-output stable regarding the static uncertainty as the extal input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-out-put stability for the closed-loop system and thus solves the origin-al GROORP. Simulation results validates the efficacy of the pro-posed methodology.