Abstract

We propose a multi-task-learning-aided knowledge transferring approach for effective and scalable deep reinforcement learning in EONs. Case studies with RMSA show that this approach can achieve 4× learning time reduction and 17.7% lower blocking probability.

© 2020 The Author(s)

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