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IEEE/ICACT20260115 Slide.16        [Big Slide]	Oral Presentation
	・Finally, I will summarize our work and discuss future directions. ・Conventional rule-based algorithms have limitations in adapting to unseen network environments. ・To address this issue, we proposed RL-HKD, a basic RL-based congestion control algorithm. ・We defined four actions with different cwnd growth rates. ・As key results, RL-HKD achieves up to 1.39% improvement over NewReno and up to 0.88% improvement over RL-TCP in bandwidth utilization. ・RL-HKD also achieves faster throughput recovery than CUBIC and better fairness than both CUBIC and NewReno. ・For future work, we consider two main directions. ・1st, we will optimize the action space and neural network for broadband network. ・2nd, we will evaluate the proposed algorithm under more dynamic network conditions, such as wireless environments.

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