This article provides a methodology and open-source implementation of
Reinforcement Learning algorithms for finding optimal routes in a
packet-optical network scenario. The algorithm uses measurements provided by
the physical layer (pre-FEC bit error rate and propagation delay) and the link
layer (link load) to configure a set of latency-based rewards and penalties
based on such measurements. Then, the algorithm executes Q-learning based on
this set of rewards for finding the optimal routing strategies. It is further
shown that the algorithm dynamically adapts to changing network conditions by
re-calculating optimal policies upon either link load changes or link
degradation as measured by pre-FEC BER.

这篇论文提供了一种方法和开源实现，用于在包光网络场景中寻找最佳路径的强化学习算法。该算法利用物理层（前 FEC 误码率和传播延迟）和链路层（链路负载）提供的测量结果，配置基于延迟的奖励和惩罚集，然后基于这些测量结果执行 Q 学习，找到最佳的路由策略。进一步证明该算法通过根据链路负载变化或以前 FEC 误码率衡量的链路退化重新计算最佳策略，动态适应不断变化的网络条件。