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Deep Reinforcement Learning Multi-Agent System for Resource Allocation in Industrial Internet of Things.

Julia Rosenberger1, Michael Urlaub1, Felix Rauterberg1

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Summary

Deep reinforcement learning (DRL) optimizes resource allocation for industrial edge devices in the Industrial Internet of Things (IIoT). This intelligent approach enhances device performance and ensures learned behaviors transfer effectively to real-world systems.

Keywords:
Industrial Internet of Thingsdeep reinforcement learningdynamic networkload balancingmulti-agent systemresource allocation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Industrial Engineering

Background:

  • The Industrial Internet of Things (IIoT) faces challenges with devices having limited computational and communication resources.
  • Industry 4.0 necessitates edge computing for data processing, further constrained by available resources.
  • Deep Reinforcement Learning (DRL) and Multi-Agent Systems (MASs) show promise for industrial applications like robotics and scheduling.

Purpose of the Study:

  • To apply DRL for intelligent resource allocation in industrial edge devices.
  • To achieve optimal utilization of limited resources in IIoT devices.
  • To leverage MASs for decentralized decision-making in complex IIoT environments.

Main Methods:

  • A network of physical and virtualized IIoT devices was constructed.
  • Deep Reinforcement Learning (DRL) was employed for resource allocation strategies.
  • Multi-Agent Systems (MASs) were utilized for decentralized control and decision-making.
  • Performance was evaluated based on MAS overhead, resource usage improvement, latency, and error rates.

Main Results:

  • The proposed DRL-based MAS approach effectively managed dynamic system changes.
  • MAS agents demonstrated very low resource consumption (traffic, computation, time).
  • The system achieved significant improvements in device resource utilization.
  • Low latency and error rates were observed during performance evaluation.

Conclusions:

  • DRL-powered MASs provide an efficient solution for resource allocation in constrained IIoT environments.
  • The developed approach is robust and adaptable to dynamic industrial settings.
  • The learned resource allocation policies are transferable from simulation to real-world IIoT systems.