  Dense and unplanned IEEE 802.11 Wireless Fidelity(Wi-Fi) deployments and the
continuous increase of throughput and latency stringent services for users have
led to machine learning algorithms to be considered as promising techniques in
the industry and the academia. Specifically, the ongoing IEEE 802.11be EHT --
Extremely High Throughput, known as Wi-Fi 7 -- amendment propose, for the first
time, Multi-Link Operation (MLO). Among others, this new feature will increase
the complexity of channel selection due the novel multiple interfaces proposal.
In this paper, we present a Parallel Transfer Reinforcement Learning
(PTRL)-based cooperative Multi-Agent Reinforcement Learning (MARL) algorithm
named Parallel Transfer Reinforcement Learning Optimistic-Weighted Value
Decomposition Networks (oVDN) to improve intelligent channel selection in IEEE
802.11be MLO-capable networks. Additionally, we compare the impact of different
parallel transfer learning alternatives and a centralized non-transfer MARL
baseline. Two PTRL methods are presented: Multi-Agent System (MAS) Joint
Q-function Transfer, where the joint Q-function is transferred and MAS
Best/Worst Experience Transfer where the best and worst experiences are
transferred among MASs. Simulation results show that oVDNg -- only the best
experiences are utilized -- is the best algorithm variant. Moreover, oVDNg
offers a gain up to 3%, 7.2% and 11% when compared with VDN, VDN-nonQ and
non-PTRL baselines. Furthermore, oVDNg experienced a reward convergence gain in
the 5 GHz interface of 33.3% over oVDNb and oVDN where only worst and both
types of experiences are considered, respectively. Finally, our best PTRL
alternative showed an improvement over the non-PTRL baseline in terms of speed
of convergence up to 40 episodes and reward up to 135%.
