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Adaptive Chaos Algorithm Reinforcement Learning Integration

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This paper presents an innovative reinforcement learning algorithm designed to enhance the performance of chaotic systems through the integration of adaptive chaos algorithms. The core of this approach lies in employing reinforcement learning to train an adaptive chaos algorithm that dynamically adjusts its parameters to optimize the system's behavior. We demonstrate the effectiveness of this method by providing a case study illustrating its ability to improve system stability and resilience in a complex, nonlinear system. The algorithm's self-tuning nature offers a significant advancement over traditional methods, addressing a critical challenge in chaotic system control. The study highlights the potential of this integrated approach to unlock new capabilities within chaotic dynamics.

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