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

• URL: http://arxiv.org/abs/2608.19049v1
• Date: Wed, 19 Aug 2026 15:41:32 GMT
• Title: Multi-Agent Off-Policy Deep Reinforcement Learning for Smart Campus Coverage
• Abstract summary: Deep reinforcement learning (DRL) has recently gained a great attention to its real-time adaptation and effectiveness in complex optimization problems.<n>This paper investigates efficient computational users in a dense multi-agent framework.
• Score: 0.6999740786886536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning (DRL) has recently gained a great attention due to its real-time adaptation and effectiveness in complex optimization problems. This paper investigates the optimal deployment of millimeter-wave (mmWave) base stations (BSs) in a realistic, non-convex campus topology. The optimization problem is NP-hard, due to the non-convex, non-smooth nature of the max-min fairness objective. To overcome these constraints, we formulate the BS placement as a Markov Decision Process (MDP) and systematically benchmark four DRL schemes: a discrete single-agent Deep Q-Network (DQN), a spatially partitioned Multi-Agent DQN, a continuous single-agent Deep Deterministic Policy Gradient (DDPG), and a geographically partitioned multi-agent DDPG framework. Numerical evaluations reveal that the multi-agent DDPG approach substantially outperforms single-agent in dense scenarios. Additionally full coverage is achieved, and a fairness Jain's index of 0.94 is obtained. Finally, the multi-agent demonstrates highly efficient computational convergence of dense scenarios with $400$ users.

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