Arm order recognition in multi-armed bandit problem with laser chaos time series
This incremental improvement addresses the need for accurate arm order information in resource allocation for sectors like information and communications technology.
The paper tackles the problem of correctly recognizing the order of arms in multi-armed bandit problems, where previous methods only identified the best arm. The result shows significant improvement in arm order recognition accuracy with reduced dependence on reward environments, while maintaining total reward comparable to conventional methods.
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.