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Question	The paper proposes a partial charging scheduling scheme based on deep reinforcement learning. How do you think this method addresses the computational complexity issues that may arise as the number of sensor nodes increases?
Answer by Author	The proposed partial charging scheduling scheme based on deep reinforcement learning (DRL) addresses computational complexity by leveraging the ability of DRL algorithms to handle large and dynamic environments efficiently. DRL can learn optimal charging policies through interaction with the environment, allowing it to adapt and scale as the number of sensor nodes increases.

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ICACT20250091 Question.1 Questioner: litingwei@stu.scau.edu.cn 2025-03-07 오전 11:57:10	ICACT20250091 Answer.1 Answer by Auhor litingwei@stu.scau.edu.cn 2025-03-07 오전 11:57:10 Chrome Click!!
The paper proposes a partial charging scheduling scheme based on deep reinforcement learning. How do you think this method addresses the computational complexity issues that may arise as the number of sensor nodes increases?	The proposed partial charging scheduling scheme based on deep reinforcement learning (DRL) addresses computational complexity by leveraging the ability of DRL algorithms to handle large and dynamic environments efficiently. DRL can learn optimal charging policies through interaction with the environment, allowing it to adapt and scale as the number of sensor nodes increases.