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Photonic artificial intelligence has attracted considerable interest inaccelerating machine learning; however, the unique optical properties have notbeen fully utilized for achieving higher-order functionalities. Chaoticitinerancy, with its spontaneous transient dynamics among multiplequasi-attractors, can be employed to realize brain-like functionalities. Inthis paper, we propose a method for controlling the chaotic itinerancy in amulti-mode semiconductor laser to solve a machine learning task, known as themulti-armed bandit problem, which is fundamental to reinforcement learning. Theproposed method utilizes ultrafast chaotic itinerant motion in mode competitiondynamics controlled via optical injection. We found that the explorationmechanism is completely different from a conventional searching algorithm andis highly scalable, outperforming the conventional approaches for large-scalebandit problems. This study paves the way to utilize chaotic itinerancy foreffectively solving complex machine learning tasks as photonic hardwareaccelerators.

Controlling chaotic itinerancy in laser dynamics for reinforcement learning