One of the most important steps in designing a model predictive control strategy is selecting appropriate parameters for the relative weights of the objective function. Typically, these are selected through trial and error to meet the desired performance. In this paper, a reinforcement learning technique called learning automata is used to select appropriate parameters for the controller of a differential drive robot through a simulation process. Results of the simulation show that the parameters always converge, although to different values. A controller chosen by the learning process is then ported to a real platform. The selected controller is shown to control the robot better than a standard model predictive control. KEYWORDS feedback linearization, machine learning, model predictive control, reinforcement learning Abbreviations: FALA, finite action-set learning automata; LA, learning automata; MPC, model predictive control; RL, reinforcement learning.410

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