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A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
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TLDR
A Deep-Reinforcement Learning agent that optimizes routing that adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay is designed and evaluated.Abstract:
In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.read more
Citations
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Journal ArticleDOI
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nguyen Cong Luong,Dinh Thai Hoang,Shimin Gong,Dusit Niyato,Ping Wang,Ying-Chang Liang,Dong In Kim +6 more
TL;DR: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection.
Journal ArticleDOI
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
TL;DR: A comprehensive survey of the applications of DL algorithms for different network layers, including physical layer modulation/coding, data link layer access control/resource allocation, and routing layer path search, and traffic balancing is performed.
Journal ArticleDOI
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges
TL;DR: This paper provides a comprehensive survey on the literature involving machine learning algorithms applied to SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security.
Posted Content
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nguyen Cong Luong,Dinh Thai Hoang,Shimin Gong,Dusit Niyato,Ping Wang,Ying-Chang Liang,Dong In Kim +6 more
TL;DR: In this paper, a comprehensive literature review on applications of deep reinforcement learning in communications and networking is presented, which includes dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation.
Journal ArticleDOI
A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning
TL;DR: This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Posted Content
Continuous control with deep reinforcement learning
Timothy P. Lillicrap,Jonathan J. Hunt,Alexander Pritzel,Nicolas Heess,Tom Erez,Yuval Tassa,David Silver,Daan Wierstra +7 more
TL;DR: This work presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces, and demonstrates that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.
The omnet++ discrete event simulation system
TL;DR: OMNeT++ is fully programmable and modular, and it was designed from the ground up to support modeling very large networks built from reusable model components.
Proceedings Article
Deterministic Policy Gradient Algorithms
TL;DR: This paper introduces an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy and demonstrates that deterministic policy gradient algorithms can significantly outperform their stochastic counterparts in high-dimensional action spaces.
Posted Content
Software-Defined Networking: A Comprehensive Survey
Diego Kreutz,Fernando M. V. Ramos,Paulo Veríssimo,Christian Esteve Rothenberg,Siamak Azodolmolky,Steve Uhlig +5 more
TL;DR: Software-Defined Networking (SDN) as discussed by the authors is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network.