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.

该论文提出了一种使用强化学习和深度符号回归来处理 5G 时代中超低时延和高吞吐场景下拥塞控制问题的方法。通过训练一个专门针对类似前传网络的拥塞控制策略，并采集基准策略的状态 - 动作经验，使用深度符号回归方法处理实时推理的挑战，从而实现基准策略的性能和泛化能力。该方法能够近似基准性能（链路利用率、延迟和公平性）并可以直接在任何编程语言中实现，并对闭合形式表达式的内部工作进行了分析。