Arm order recognition in multi-armed bandit problem with laser chaos time series

TL;DR

This paper introduces an adaptive algorithm using laser chaos time series for multi-armed bandit problems, improving arm order recognition accuracy while maintaining high total rewards, applicable in resource allocation sectors.

Contribution

It presents a novel adaptive exploration control method based on confidence intervals, enhancing arm order recognition in laser chaos-based MAB algorithms.

Findings

Improved arm order recognition accuracy.

Reduced dependence on reward environment variations.

Maintained total reward levels.

Abstract

By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward expectation, the correct recognition of the order of arms in terms of reward expectations is not achievable. Here, we present an algorithm where the degree of exploration is adaptively controlled based on confidence intervals that represent the estimation accuracy of reward expectations. We have demonstrated numerically that our approach did improve arm order recognition accuracy significantly, along with reduced dependence on reward environments, and the total reward is almost maintained compared with conventional MAB methods. This study applies to sectors where the order information is critical, such as efficient allocation of resources in information and communications technology.