  WiFi densification leads to the existence of multiple overlapping coverage
areas, which allows user stations (STAs) to choose between different Access
Points (APs). The standard WiFi association method makes the STAs select the AP
with the strongest signal, which in many cases leads to underutilization of
some APs while overcrowding others. To mitigate this situation, Reinforcement
Learning techniques such as Multi-Armed Bandits can be used to dynamically
learn the optimal mapping between APs and STAs, and so redistribute the STAs
among the available APs accordingly. This is an especially challenging problem
since the network response observed by a given STA depends on the behavior of
the others, and so it is very difficult to predict without a global view of the
network. In this paper, we focus on solving this problem in a decentralized
way, where STAs independently explore the different APs inside their coverage
range, and select the one that better satisfy its needs. To do it, we propose a
novel approach called Opportunistic {\epsilon}-greedy with Stickiness that
halts the exploration when a suitable AP is found, then, it remains associated
to it while the STA is satisfied, only resuming the exploration after several
unsatisfactory association periods. With this approach, we reduce significantly
the network response variability, improving the ability of the STAs to find a
solution faster, as well as achieving a more efficient use of the network
resources.
