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

arXiv: https://arxiv.org/abs/2608.19049 · HTML: https://mlsanity.com/paper/2608.19049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [Multi-Objective Deep Reinforcement Learning for 5G Base Station Placement to Support Localisation for Future Sustainable Traffic](https://mlsanity.com/paper/2404.14954/) (2024)
- [Enhanced 5G/B5G Network Planning/Optimization deploying RIS in Urban/Outdoor Scenarios](https://mlsanity.com/paper/2412.05385/) (2024)
- [Hybrid Deep Reinforcement Learning for Joint Resource Allocation in Multi-Active RIS-Aided Uplink Communications](https://mlsanity.com/paper/2512.22107/) (2025)

## Abstract (arXiv)

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.