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Spectrum allocation in the form of primary channel and bandwidth selection isa key factor for dynamic channel bonding (DCB) wireless local area networks(WLANs). To cope with varying environments, where networks change theirconfigurations on their own, the wireless community is looking towardssolutions aided by machine learning (ML), and especially reinforcement learning(RL) given its trial-and-error approach. However, strong assumptions arenormally made to let complex RL models converge to near-optimal solutions. Ourgoal with this paper is two-fold: justify in a comprehensible way why RL shouldbe the approach for wireless networks problems like decentralized spectrumallocation, and call into question whether the use of complex RL algorithmshelps the quest of rapid learning in realistic scenarios. We derive thatstateless RL in the form of lightweight multi-armed-bandits (MABs) is anefficient solution for rapid adaptation avoiding the definition of extensive ormeaningless RL states.

Stateless Reinforcement Learning for Multi-Agent Systems: the Case of Spectrum Allocation in Dynamic Channel Bonding WLANs