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This work discusses a closed-loop control strategy for complex systemsutilizing scarce and streaming data. A discrete embedding space is first builtusing hash functions applied to the sensor measurements from which a Markovprocess model is derived, approximating the complex system's dynamics. Acontrol strategy is then learned using reinforcement learning once rewardsrelevant with respect to the control objective are identified. This method isdesigned for experimental configurations, requiring no computations nor priorknowledge of the system, and enjoys intrinsic robustness. It is illustrated ontwo systems: the control of the transitions of a Lorenz 63 dynamical system,and the control of the drag of a cylinder flow. The method is shown to performwell.

A statistical learning strategy for closed-loop control of fluid flows