Optimizing Resource Allocation with Reinforcement Learning and Genetic Algorithms: An AI-Driven Approach

Abstract

This research explores the fusion of reinforcement learning and genetic algorithms to optimize resource allocation in complex systems. The study presents an AI-driven framework that synergizes the adaptive capabilities of reinforcement learning with the evolutionary search mechanisms of genetic algorithms, aiming to enhance decision-making processes in dynamic environments. By integrating these methodologies, the proposed model leverages the iterative improvement strengths of genetic algorithms to initialize and guide the exploration-exploitation balance in reinforcement learning, while the policy refinement in reinforcement learning subsequently informs and refines the genetic algorithm's search space. Rigorous experimentation across various resource allocation scenarios, including network bandwidth distribution and computational resource management, demonstrates the framework's superiority in achieving near-optimal solutions compared to traditional approaches. Results highlight significant improvements in efficiency, adaptability, and stability, with the hybrid model consistently outperforming baseline models in convergence speed and solution quality. This research underscores the potential of combining reinforcement learning and genetic algorithms to address intricate resource allocation challenges, suggesting pathways for further exploration in AI-driven optimization strategies.