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#reinforcement learning Open access

Title: Algorithmic Chaos Theory with Dynamic Parameter Tuning

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This paper explores the development of an algorithmic chaos theory system designed to generate complex, multi-dimensional chaos patterns without relying on predefined, fixed parameters. Leveraging a reinforcement learning-inspired approach, the algorithm continuously adjusts its internal parameters based on observed system behavior, fostering emergent chaos generation. We propose a novel framework that moves beyond traditional chaotic systems, emphasizing dynamic adaptation and self-governing behavior, ultimately aiming to create systems exhibiting a higher degree of unpredictability. The core mechanism centers around a feedback loop where the algorithm's output is evaluated, and adjustments are made to its internal state, driving the creation of intricate chaotic configurations. This research addresses a key challenge in chaos theory – the requirement for adaptive systems – and offers a new methodology for exploring and generating complex dynamic systems.

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