The complexity of emerging sixth-generation (6G) wireless networks has
sparked an upsurge in adopting artificial intelligence (AI) to underpin the
challenges in network management and resource allocation under strict service
level agreements (SLAs). It inaugurates the era of massive network slicing as a
distributive technology where tenancy would be extended to the final consumer
through pervading the digitalization of vertical immersive use-cases. Despite
the promising performance of deep reinforcement learning (DRL) in network
slicing, lack of transparency, interpretability, and opaque model concerns
impedes users from trusting the DRL agent decisions or predictions. This
problem becomes even more pronounced when there is a need to provision highly
reliable and secure services. Leveraging eXplainable AI (XAI) in conjunction
with an explanation-guided approach, we propose an eXplainable reinforcement
learning (XRL) scheme to surmount the opaqueness of black-box DRL. The core
concept behind the proposed method is the intrinsic interpretability of the
reward hypothesis aiming to encourage DRL agents to learn the best actions for
specific network slice states while coping with conflict-prone and complex
relations of state-action pairs. To validate the proposed framework, we target
a resource allocation optimization problem where multi-agent XRL strives to
allocate optimal available radio resources to meet the SLA requirements of
slices. Finally, we present numerical results to showcase the superiority of
the adopted XRL approach over the DRL baseline. As far as we know, this is the
first work that studies the feasibility of an explanation-guided DRL approach
in the context of 6G networks.Comment: 6 Pages, 6 figure