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IEEE/ICACT20260115 Slide.05        [Big Slide]	Oral Presentation
	・Here, I introduce several existing studies on machine learning–based congestion control algorithms. ・The first example is Remy, which is one of the earliest machine learning–based congestion control algorithms. ・However, since Remy relies on offline training, it has difficulty adapting to unseen network environments. ・The second example is QTCP, which applies reinforcement learning and enables online learning in real time. ・However, its cwnd update amount is fixed at the byte level, leading to an action space with limited flexibility. ・From these observations, it can be seen that reinforcement learning–based algorithms capable of adapting to changing environments remain insufficient.

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