  Spectrum allocation in the form of primary channel and bandwidth selection is
a key factor for dynamic channel bonding (DCB) wireless local area networks
(WLANs). To cope with varying environments, where networks change their
configurations on their own, the wireless community is looking towards
solutions aided by machine learning (ML), and especially reinforcement learning
(RL) given its trial-and-error approach. However, strong assumptions are
normally made to let complex RL models converge to near-optimal solutions. Our
goal with this paper is two-fold: justify in a comprehensible way why RL should
be the approach for wireless networks problems like decentralized spectrum
allocation, and call into question whether the use of complex RL algorithms
helps the quest of rapid learning in realistic scenarios. We derive that
stateless RL in the form of lightweight multi-armed-bandits (MABs) is an
efficient solution for rapid adaptation avoiding the definition of extensive or
meaningless RL states.
