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Controlling chaotic itinerancy in laser dynamics for reinforcement learning.

Ryugo Iwami1, Takatomo Mihana1, Kazutaka Kanno1

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This study demonstrates using chaotic itinerancy in lasers to solve complex machine learning problems. This photonic approach offers a scalable, brain-like alternative to conventional algorithms for reinforcement learning tasks.

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Area of Science:

  • Photonics
  • Artificial Intelligence
  • Machine Learning
  • Chaos Theory

Background:

  • Photonic artificial intelligence shows promise for accelerating machine learning.
  • Higher-order functionalities using unique optical properties remain underexplored.
  • Chaotic itinerancy, characterized by transient dynamics among quasi-attractors, offers potential for brain-like functionalities.

Purpose of the Study:

  • To investigate controlling chaotic itinerancy in a multimode semiconductor laser.
  • To apply this control to solve the multiarmed bandit problem, a fundamental reinforcement learning task.
  • To explore the potential of chaotic itinerancy as a photonic hardware accelerator.

Main Methods:

  • Numerical and experimental investigation of chaotic itinerancy control.
  • Utilizing chaotic itinerant motion in mode competition dynamics.
  • Controlling dynamics via optical injection in a multimode semiconductor laser.

Main Results:

  • Demonstrated control of chaotic itinerancy for machine learning.
  • The exploration mechanism differs significantly from conventional search algorithms.
  • The proposed method shows high scalability and outperforms conventional approaches for large-scale bandit problems.

Conclusions:

  • Chaotic itinerancy can be effectively controlled in photonic systems.
  • This approach offers a novel, brain-like mechanism for solving machine learning tasks.
  • Paves the way for using chaotic itinerancy in photonic hardware accelerators for complex tasks.