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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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NF-MORL: a neuro-fuzzy multi-objective reinforcement learning framework for task scheduling in fog computing

Xiaomo Yu1,2,3, Ling Tang4, Jie Mi2

  • 1Guangxi Colleges and Universities Key laboratory of Intelligent Logistics Technology, Nanning Normal University, Nanning, 530001, Guangxi, China.

Scientific Reports
|December 26, 2025
PubMed
Summary

This study introduces Neuro-Fuzzy Multi-Objective Reinforcement Learning (NF-MORL) for efficient task scheduling in fog networks. NF-MORL significantly improves performance metrics like makespan, energy use, cost, and reliability.

Keywords:
Energy efficiencyFault toleranceFog computingMulti-objective reinforcement learningNeuro-fuzzy systemsTask scheduling

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Computing

Background:

  • The Internet of Things (IoT) generates vast data, straining traditional cloud computing due to latency and centralization.
  • Fog computing offers a decentralized solution by moving computation closer to data sources.
  • Effective task scheduling in dynamic, heterogeneous fog environments remains a significant challenge.

Purpose of the Study:

  • To develop an innovative framework for task scheduling in fog networks.
  • To address the limitations of conventional cloud and existing fog computing approaches.
  • To enhance efficiency, reduce latency, and improve reliability in fog environments.

Main Methods:

  • Introduced A Neuro-Fuzzy Multi-Objective Reinforcement Learning (NF-MORL) framework.
  • Integrated Takagi-Sugeno fuzzy logic for uncertainty handling and priority interpretation.
  • Employed a multi-objective actor-critic agent for learning to balance makespan, energy, cost, and reliability.

Main Results:

  • NF-MORL reduced makespan by up to 35%.
  • Achieved approximately 30% enhancement in energy efficiency.
  • Decreased operational costs by up to 40%.
  • Augmented fault tolerance by as much as 37%.

Conclusions:

  • NF-MORL demonstrates superior performance compared to state-of-the-art techniques.
  • The framework effectively adapts to varying workload sizes and dynamic conditions.
  • Combining fuzzy logic with reinforcement learning creates resilient and efficient fog schedulers.