  Photonic computing has been widely used to accelerate the computational
performance in machine learning. Photonic decision-making is a promising
approach that uses photonic computing technologies to solve the multi-armed
bandit problem based on reinforcement learning. Photonic decision making using
chaotic mode competition dynamics has been proposed. However, the experimental
conditions for achieving superior decision-making performance have not yet been
established. In this study, we experimentally investigate the mode competition
dynamics in a chaotic multimode semiconductor laser in the presence of optical
feedback and injection. We control chaotic mode-competition dynamics via
optical injection, and we found that positive wavelength detuning results in an
efficient mode concentration to one of the longitudinal modes with a small
optical injection power. We experimentally investigate two-dimensional
bifurcation map of the laser dynamics of the total intensity. Complex mixed
dynamics are observed in the presence of optical feedback and injection. We
experimentally conduct decision making to solve the bandit problem using
chaotic mode-competition dynamics. A fast mode concentration property is
observed at positive wavelength detuning, which resulted in a fast convergence
of the correct decision rate. Our findings could be useful in accelerating the
decision-making performance in adaptive optical networks using reinforcement
learning.
