Recent advances in TCP congestion control (CC) have achieved tremendous
success with deep reinforcement learning (RL) approaches, which use feedforward
neural networks (NN) to learn complex environment conditions and make better
decisions. However, such "black-box" policies lack interpretability and
reliability, and often, they need to operate outside the traditional TCP
datapath due to the use of complex NNs. This paper proposes a novel two-stage
solution to achieve the best of both worlds: first to train a deep RL agent,
then distill its (over-)parameterized NN policy into white-box, light-weight
rules in the form of symbolic expressions that are much easier to understand
and to implement in constrained environments. At the core of our proposal is a
novel symbolic branching algorithm that enables the rule to be aware of the
context in terms of various network conditions, eventually converting the NN
policy into a symbolic tree. The distilled symbolic rules preserve and often
improve performance over state-of-the-art NN policies while being faster and
simpler than a standard neural network. We validate the performance of our
distilled symbolic rules on both simulation and emulation environments. Our
code is available at this https URL.

本文提出一种基于深度强化学习的深度神经网络模型，在模型训练完成后对模型参数进行压缩，转换成可解释且能够适用于不同网络环境的符号表达式，并提供了一种新的符号分支算法，验证了该方法在模拟和仿真环境中能够保留和提高性能。