This paper addresses the use of Deep Reinforcement Learning for automatic routing in Optical Transport Networks at the electrical-layer level. We propose a DRL-based solution that achieves both high performance and fast learning.Peer ReviewedPostprint (published version
Abstract In quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resourc...
International audienceReinforcement learning (RL), which is a class of machine learning, provides a ...
We present a solution based on deep reinforcement learning (DRL) that jointly addresses spectrum all...
Deep reinforcement learning (DRL) has recently revolutionized the resolution of decision-making and ...
Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization problems in...
This paper evaluates different aspects of the performance of a solution for routing and spectrum all...
Conventional schemes for service provisioning in next-generation elastic optical networks (EONs) rel...
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated ...
In this paper, a deep reinforcement learning routing(DRL-Routing) algorithm was proposed to solve th...
International audienceKnowledge-Defined networking (KDN) is a concept that relies on Software-Define...
Deep reinforcement learning (DRL) enables autonomic optical networking by allowing the network contr...
Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a dramatic improvement...
The digital transformation is pushing the existing network technologies towards new horizons, enabli...
Packet routing problem most commonly emerges in the context of computer networks, thus the majority ...
A deep reinforcement learning-based agent is presented to perform autonomous lightpath restoration u...
Abstract In quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resourc...
International audienceReinforcement learning (RL), which is a class of machine learning, provides a ...
We present a solution based on deep reinforcement learning (DRL) that jointly addresses spectrum all...
Deep reinforcement learning (DRL) has recently revolutionized the resolution of decision-making and ...
Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization problems in...
This paper evaluates different aspects of the performance of a solution for routing and spectrum all...
Conventional schemes for service provisioning in next-generation elastic optical networks (EONs) rel...
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated ...
In this paper, a deep reinforcement learning routing(DRL-Routing) algorithm was proposed to solve th...
International audienceKnowledge-Defined networking (KDN) is a concept that relies on Software-Define...
Deep reinforcement learning (DRL) enables autonomic optical networking by allowing the network contr...
Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a dramatic improvement...
The digital transformation is pushing the existing network technologies towards new horizons, enabli...
Packet routing problem most commonly emerges in the context of computer networks, thus the majority ...
A deep reinforcement learning-based agent is presented to perform autonomous lightpath restoration u...
Abstract In quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resourc...
International audienceReinforcement learning (RL), which is a class of machine learning, provides a ...
We present a solution based on deep reinforcement learning (DRL) that jointly addresses spectrum all...
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