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This paper focuses on the active flow control (AFC) of the flow over acircular cylinder with synthetic jets through deep reinforcement learning (DRL)by implementing a reward function based on dynamic mode decomposition (DMD). Asa main factor that affects the DRL model, the reward is determined by theinformation extracted from flow field by performing DMD on measurements throughsimulation. With the data-driven reward, the DRL model is able to learn the AFCpolicy through the more global information of the field, and instructs the massflow rate of the synthetic jets. As a result of this type of AFC, the vortexstreet is stabilized with a reduction of approximately 8% in drag and animprovement of approximately 109% in recirculation area. Furthermore, theconfiguration of the flow modified by the AFC is studied with DMD on thevelocity measurement of the complete flow field.

An application of data driven reward of deep reinforcement learning by dynamic mode decomposition in active flow control