  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.
