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Learning how to control congestion remains a challenge despite years of progress. Existing congestion control protocols have demonstrated efficacy within specific network conditions, inevitably behaving suboptimally or poorly in others. Machine learning solutions to congestion control have been proposed, though relying on extensive training and specific network configurations. In this paper, we loosen such dependencies by proposing Mutant, an online reinforcement learning algorithm for congestion control that adapts to the behavior of the best-performing schemes, outperforming them in most network conditions. Design challenges included determining the best protocols to learn from, given a network scenario, and creating a system able to evolve to accommodate future protocols with minimal changes. Our evaluation on real-world and emulated scenarios shows that Mutant achieves lower delays and higher throughput than prior learning-based schemes while maintaining fairness by exhibiting negligible harm to competing flows, making it robust across diverse and dynamic network conditions 

Despite years of research on transport protocols, the tussle between in-network and end-to-end congestion control has not been solved. This debate is due to the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches. In this paper, we present Owl, a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. We show that our solution converges to a fair resource allocation after the learning overhead. Our kernel implementation, deployed over emulated and large scale virtual network testbeds, outperforms all benchmark solutions based on end-to-end or in-network congestion control.