This paper presents a hybrid adaptive dynamic programming (hybrid-ADP) approach for determining the optimal continuous and discrete control laws of a switched system online, solely from state observations. The new hybrid-ADP recurrence relationships presented are applicable to model-free control of switched hybrid systems that are possibly nonlinear. The computational complexity and convergence of the hybrid-ADP approach are analyzed, and the method is validated numerically showing that the optimal controller and value function can be learned iteratively online from state observations.