Computational approach for deriving optimal design laws from the data. (A) Pre-process the actions and outputs of the dynamical system and construct the feedforward signals that will be used for the feedback gain learning and the design of an online real-time control loop (Supplementary information, Section 2A). (B) Measure the input-output data, as well as the feedforward signals, over discrete-time series, based on which the discrete-time data samples are assembled using the tensor product (Supplementary information, Section 2B). (C) This part is central for learning the feedback gain $K_{\text{o}}^{*}$ from discrete-time data. First, calculate the Bellman equation for optimality via policy iterations. Then, through policy evaluation and improvement, the optimal feedback gain is obtained from the discrete-time data samples with rigorous mathematical operations and convergence deduction (Supplementary information, Section 2C). Finally, both the feedforward signal in (A) and the feedback gain $K_{\text{o}}^{*}$ contribute to the optimal decision law in eq. (5).