  This paper proposes DeepRMSA, a deep reinforcement learning framework for
routing, modulation and spectrum assignment (RMSA) in elastic optical networks
(EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the
policies with deep neural networks (DNNs) that can sense complex EON states.
The DNNs are trained with experiences of dynamic lightpath provisioning. We
first modify the asynchronous advantage actor-critic algorithm and present an
episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP
divides the dynamic provisioning process into multiple episodes (each
containing the servicing of a fixed number of lightpath requests) and performs
training by the end of each episode. The optimization target of DeepRMSA-EP at
each step of servicing a request is to maximize the cumulative reward within
the rest of the episode. Thus, we obviate the need for estimating the rewards
related to unknown future states. To overcome the instability issue in the
training of DeepRMSA-EP due to the oscillations of cumulative rewards, we
further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX.
DeepRMSA-FLX attempts to smooth out the oscillations by defining the
optimization scope at each step as a sliding window, and ensuring that the
cumulative rewards always include rewards from a fixed number of requests.
Evaluations with the two sample topologies show that DeepRMSA-FLX can
effectively stabilize the training while achieving blocking probability
reductions of more than 20.3% and 14.3%, when compared with the baselines.
