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

arXiv: https://arxiv.org/abs/2005.13085 · HTML: https://mlsanity.com/paper/2005.13085/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 2 of 2)

- [Controlling chaotic itinerancy in laser dynamics for reinforcement learning](https://mlsanity.com/paper/2205.05987/) (2022)
- [Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning](https://mlsanity.com/paper/2507.05900/) (2025)

## Abstract (arXiv)

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.