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IEEE/ICACT20260115 Question.3 Questioner: md.t.a.812@ms.saitama-u.ac.jp 2026-02-10 오후 11:36:33	IEEE/ICACT20260115 Answer.3 Answer by Auhor aoki.kazuma.s1@elms.hokudai.ac.jp 2026-02-10 오후 11:36:33
What was the rationale for selecting reinforcement learning over a hybrid or lightweight deep learning model?	In this study, we chose reinforcement learning because it enables online machine learning to adapt to dynamically fluctuating network environments. Furthermore, while the DQN used in this study is expected to present computational bottlenecks for practical implementation, since this method targets only sender-side cwnd control, its feasibility is considered relatively high if deployed on the server side rather than edge devices. Additionally, advancements in both hardware and software, such as GPU utilization and lightweight inference processing, are expected to make real-world implementation increasingly feasible in the future.

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