  The Open Radio Access Network (RAN) paradigm is transforming cellular
networks into a system of disaggregated, virtualized, and software-based
components. These self-optimize the network through programmable, closed-loop
control, leveraging Artificial Intelligence (AI) and Machine Learning (ML)
routines. In this context, Deep Reinforcement Learning (DRL) has shown great
potential in addressing complex resource allocation problems. However, DRL
-based solutions are inherently hard to explain, which hinders their deployment
and use in practice. In this paper, we propose EXPLORA, a framework that
provides explainability of DRL-based control solutions for the Open RAN
ecosystem. EXPLORA synthesizes network-oriented explanations based on an
attributed graph that produces a link between the actions taken by a DRL agent
(i.e., the nodes of the graph) and the input state space (i.e., the attributes
of each node). This novel approach allows EXPLORA to explain models by
providing information on the wireless context in which the DRL agent operates.
EXPLORA is also designed to be lightweight for real-time operation. We
prototype EXPLORA and test it experimentally on an O-RAN-compliant
near-real-time RIC deployed on the Colosseum wireless network emulator. We
evaluate EXPLORA for agents trained for different purposes and showcase how it
generates clear network-oriented explanations. We also show how explanations
can be used to perform informative and targeted intent-based action steering
and achieve median transmission bitrate improvements of 4% and tail
improvements of 10%.
