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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Related Experiment Video

Updated: Aug 15, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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AQMDRL: Automatic Quality of Service Architecture Based on Multistep Deep Reinforcement Learning in Software-Defined

Junyan Chen1,2,3, Cenhuishan Liao1, Yong Wang1

  • 1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

This study introduces an automatic Quality of Service (QoS) architecture using multistep deep reinforcement learning (DRL) to enhance Software-Defined Networking (SDN). The new method improves convergence stability and reduces network transmission delay in SDN.

Keywords:
SumTreedeep deterministic policy gradientdeep reinforcement learningmultistepquality of servicesoftware-defined networking

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Software-Defined Networking (SDN) is crucial for data center networks, leveraging AI for global performance optimization.
  • Deep Reinforcement Learning (DRL) shows promise for intelligent routing and scheduling in SDN.
  • Traditional DRL algorithms suffer from slow convergence and instability, impacting Quality of Service (QoS).

Purpose of the Study:

  • To propose an automatic QoS architecture based on multistep DRL (AQMDRL) for optimizing SDN performance.
  • To address the overestimation and underestimation issues inherent in the Deep Deterministic Policy Gradient (DDPG) algorithm.

Main Methods:

  • AQMDRL employs a multistep approach, utilizing n-step action values instead of one-step Q-values to enhance stability.
  • Prioritized experience sampling with SumTree binary trees is adapted to accelerate the convergence rate.
  • The proposed architecture aims to improve the decision-making and generalization capabilities within SDN environments.

Main Results:

  • The AQMDRL architecture demonstrates significantly improved convergence performance compared to existing DRL algorithms.
  • Experimental results show a notable reduction in network transmission delay for SDN.
  • The multistep approach effectively mitigates Q-value overestimation/underestimation, leading to more stable learning.

Conclusions:

  • AQMDRL offers a robust solution for optimizing QoS in SDN by enhancing DRL convergence and stability.
  • The proposed method provides a foundation for more efficient and reliable data center network management.
  • This research contributes to advancing the application of AI in network engineering for improved performance.