A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

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.

References in corpus (2)

• Continuous control with deep reinforcement learning
• Deep Reinforcement Learning: An Overview

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• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
• Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning
• Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
• Towards Cognitive Routing based on Deep Reinforcement Learning
• Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Directions
• STDPG: A Spatio-Temporal Deterministic Policy Gradient Agent for Dynamic Routing in SDN
• Deep reinforcement learning for RAN optimization and control
• Relational Deep Reinforcement Learning for Routing in Wireless Networks