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Photonic reinforcement learning based on optoelectronic reservoir computing.

Kazutaka Kanno1, Atsushi Uchida2

  • 1Department of Information and Computer Sciences, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama, 338-8570, Japan. kkanno@mail.saitama-u.ac.jp.

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We introduce a novel photonic approach for reinforcement learning, significantly reducing computational costs. This method uses optoelectronic reservoir computing for faster, efficient AI applications like autonomous driving.

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Area of Science:

  • Artificial Intelligence
  • Photonics
  • Machine Learning

Background:

  • Reinforcement learning (RL) is crucial for AI tasks without training data, but deep neural networks incur high computational costs.
  • Reducing the computational expense of RL remains a significant challenge in AI development.

Purpose of the Study:

  • To propose and demonstrate a photonic on-line implementation of reinforcement learning.
  • To accelerate RL using optoelectronic delay-based reservoir computing.

Main Methods:

  • Experimental and numerical evaluation of a photonic reservoir computing system for RL.
  • Utilizing optoelectronic delay-based reservoir computing to eliminate internal connection weight learning.

Main Results:

  • Achieved reinforcement learning acceleration at several megahertz.
  • Successfully evaluated the scheme on benchmark tasks: CartPole-v0 and MountainCar-v0.

Conclusions:

  • This work presents the first hardware implementation of RL using photonic reservoir computing.
  • The proposed photonic accelerator offers a pathway towards fast and efficient reinforcement learning.