  As mobile networks embrace the 5G era, the interest in adopting Reinforcement
Learning (RL) algorithms to handle challenges in ultra-low-latency and high
throughput scenarios increases. Simultaneously, the advent of packetized
fronthaul networks imposes demanding requirements that traditional congestion
control mechanisms cannot accomplish, highlighting the potential of RL-based
congestion control algorithms. Although learning RL policies optimized for
satisfying the stringent fronthaul requirements is feasible, the adoption of
neural network models in real deployments still poses some challenges regarding
real-time inference and interpretability. This paper proposes a methodology to
deal with such challenges while maintaining the performance and generalization
capabilities provided by a baseline RL policy. The method consists of (1)
training a congestion control policy specialized in fronthaul-like networks via
reinforcement learning, (2) collecting state-action experiences from the
baseline, and (3) performing deep symbolic regression on the collected dataset.
The proposed process overcomes the challenges related to inference-time
limitations through closed-form expressions that approximate the baseline
performance (link utilization, delay, and fairness) and which can be directly
implemented in any programming language. Finally, we analyze the inner workings
of the closed-form expressions.
