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Sensitivity - Local index to control chaoticity or gradient globally.

Neural networks : the official journal of the International Neural Network Society·2021
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Adaptive balancing of exploration and exploitation around the edge of chaos in internal-chaos-based learning.

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Updated: Jan 15, 2026

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Dynamic reinforcement learning for actors.

Katsunari Shibata1

  • 1Independent Researcher, Kosai, Shizuoka, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|October 15, 2025
PubMed
Summary
This summary is machine-generated.

Dynamic Reinforcement Learning (RL) introduces chaotic system dynamics for improved exploration and exploitation balance. This novel approach enables adaptive learning in unfamiliar situations without external noise or complex computations.

Keywords:
Chaotic dynamicsExplorationRecurrent neural network (RNN)Reinforcement learning (RL)SensitivityThinking

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning

Background:

  • Stochastic selection in Reinforcement Learning (RL) and generative AI is limited in balancing exploration and exploitation.
  • Existing methods struggle to achieve human-like flexibility in decision-making processes.

Purpose of the Study:

  • To introduce Dynamic Reinforcement Learning (RL) as a novel approach to enhance exploration in RL.
  • To enable RL agents to learn and adapt using chaotic system dynamics for more flexible decision-making.

Main Methods:

  • Dynamic RL learns global system dynamics using a local index called sensitivity.
  • Sensitivity Adjustment Learning (SAL) prevents excessive convergence.
  • Sensitivity-controlled Reinforcement Learning (SRL) modulates dynamics for improved state transitions and exploration.

Main Results:

  • Dynamic RL was applied to the actor in an Actor-Critic framework and tested on dynamic tasks.
  • The approach demonstrated effective functioning without external exploration noise or backward computation.
  • Dynamic RL showed excellent adaptability to unfamiliar situations with controlled chaotic dynamics.

Conclusions:

  • Dynamic RL offers a significant shift from static to dynamic exploration in RL.
  • The study hypothesizes a link between exploration and thinking, suggesting Dynamic RL can facilitate this.
  • A call for discussion on the potential risks of this research is made, despite its effectiveness.