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

DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks