OPTIMIZING RESOURCE ALLOCATION IN DISTRIBUTED MACHINE LEARNING SYSTEMS: A HYBRID APPROACH COMBINING REINFORCEMENT LEARNING AND GAME THEORY
Keywords:
Reinforcement Learning (RL), Game Theory (GT), Resource Allocation, Distributed Machine Learning Systems, Hybrid Optimization FrameworkAbstract
As machine learning models grow in complexity and scale, efficient resource allocation in distributed systems becomes a critical challenge. This paper introduces a novel hybrid approach combining Reinforcement Learning (RL) and Game Theory (GT) to optimize resource allocation in distributed machine learning systems. We propose a framework that leverages RL for dynamic resource management and GT for strategic decision-making among distributed nodes. The efficacy of our approach is evaluated through simulations and real-world benchmarks, demonstrating significant improvements in system performance and resource utilization.
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©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.
