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Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
- This paper explores the use of multi-agent reinforcement learning (MARL) for managing distributed channel access in wireless local area networks (WLANs).
- The study focuses on a scenario where agents use different types of reinforcement learning algorithms, including value-based and policy-based approaches.
- A novel framework called QPMIX is proposed for heterogeneous MARL training, employing centralized training with distributed execution to facilitate collaboration among different agents.
- The research theoretically proves the convergence of the proposed heterogeneous MARL method, specifically when utilizing linear value function approximation.
- The QPMIX framework aims to maximize network throughput, ensure fairness among stations, and enhance overall network performance in WLANs.
- Simulation results reveal that the QPMIX algorithm outperforms conventional carrier-sense multiple access with collision avoidance (CSMA/CA) in terms of throughput, mean delay, delay jitter, and collision rates under saturated traffic conditions.
- Additionally, the QPMIX algorithm demonstrates robustness in scenarios with unsaturated traffic and sensitivity to delays, showcasing cooperative behavior among diverse agents in WLANs.
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