Today's networks are struggling to scale and satisfy the high number and high variety of co-existing network requirements. While existing congestion control (CC) protocols are designed to handle strict classification of network flows into one or few priorities, a more granular and dynamic congestion control is needed. In this paper we present Hercules, a novel CC protocol based on an online learning approach, which supports unbounded and continues requirements space. We implemented Hercules as a QUIC module and we show, through analytical analysis and real-world experiments, that it provides between $50\%-250\%$ higher QoS for co-existing diverse network flows and outperforms state-of-the-art CC protocols, even under high network congestion.

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