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arXiv Machine Learning By Omar Rady, Mohamed Ayman, Ali Arafa, Mohamed Shalma

Multi-Agent Off-Policy Deep Reinforcement Learning for Smart Campus Coverage

Read the original on arXiv Machine Learning →

The paper studies optimal placement of millimeter-wave base stations in a realistic, non-convex campus layout using deep reinforcement learning. It compares four DRL methods—single-agent DQN, multi-agent partitioned DQN, single-agent DDPG, and multi-agent partitioned DDPG—and finds that the multi-agent DDPG approach achieves full coverage, a Jain's fairness index of 0.94, and superior performance in dense scenarios with 400 users. The multi-agent DDPG also converges more efficiently than single-agent methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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