Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Chaos control and synchronization
Abstract
This paper explores the application of adaptive chaos theory, leveraging reinforcement learning to dynamically adjust parameters of chaotic systems. Traditional approaches often rely on fixed, static parameter sets, limiting the system's flexibility and potential for robust behavior. This research introduces a novel system that employs a reinforcement learning agent to iteratively refine parameters based on real-time feedback, offering a paradigm shift towards intelligent parameter control. The core mechanism centers around a feedback loop that continuously evaluates the system's response to changing conditions, optimizing the parameters to achieve desired outcomes. The goal is to move beyond static parameter sets and towards a system capable of adapting to complex, evolving environments.
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