  The move toward artificial intelligence (AI)-native sixth-generation (6G)
networks has put more emphasis on the importance of explainability and
trustworthiness in network management operations, especially for
mission-critical use-cases. Such desired trust transcends traditional post-hoc
explainable AI (XAI) methods to using contextual explanations for guiding the
learning process in an in-hoc way. This paper proposes a novel graph
reinforcement learning (GRL) framework named TANGO which relies on a symbolic
subsystem. It consists of a Bayesian-graph neural network (GNN) Explainer,
whose outputs, in terms of edge/node importance and uncertainty, are
periodically translated to a logical GRL reward function. This adjustment is
accomplished through defined symbolic reasoning rules within a Reasoner.
Considering a real-world testbed proof-of-concept (PoC), a gNodeB (gNB) radio
resource allocation problem is formulated, which aims to minimize under- and
over-provisioning of physical resource blocks (PRBs) while penalizing decisions
emanating from the uncertain and less important edge-nodes relations. Our
findings reveal that the proposed in-hoc explainability solution significantly
expedites convergence compared to standard GRL baseline and other benchmarks in
the deep reinforcement learning (DRL) domain. The experiment evaluates
performance in AI, complexity, energy consumption, robustness, network,
scalability, and explainability metrics. Specifically, the results show that
TANGO achieves a noteworthy accuracy of 96.39% in terms of optimal PRB
allocation in inference phase, outperforming the baseline by 1.22x.
