<ul><li>This paper explores the application of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks (WLANs).</li><li>The study focuses on the practical scenario where agents adopt heterogeneously value-based or policy-based reinforcement learning algorithms to train the model.</li><li>The researchers propose a heterogeneous MARL training framework called QPMIX, which enables collaboration among heterogeneous agents through centralized training and distributed execution.</li><li>Simulation results demonstrate that the QPMIX algorithm improves network throughput, mean delay, delay jitter, and collision rates compared to conventional carrier-sense multiple access with collision avoidance in saturated traffic scenarios.</li></ul>

Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs

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