# Scalable photonic reinforcement learning by time-division multiplexing of laser chaos

arXiv: https://arxiv.org/abs/1803.09425 · HTML: https://mlsanity.com/paper/1803.09425/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [Controlling chaotic itinerancy in laser dynamics for reinforcement learning](https://mlsanity.com/paper/2205.05987/) (2022)
- [Parallel photonic accelerator for decision making using optical spatiotemporal chaos](https://mlsanity.com/paper/2210.06976/) (2022)
- [Theory of Acceleration of Decision Making by Correlated Time Sequences](https://mlsanity.com/paper/2203.16004/) (2022)
- [Conflict-free joint decision by lag and zero-lag synchronization in laser network](https://mlsanity.com/paper/2307.15373/) (2023)
- [Parallel bandit architecture based on laser chaos for reinforcement learning](https://mlsanity.com/paper/2205.09543/) (2022)
- [Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning](https://mlsanity.com/paper/2507.05900/) (2025)
- [Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems](https://mlsanity.com/paper/2603.05559/) (2026)
- [Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip](https://mlsanity.com/paper/2609.36347/) (2026)

## Abstract (arXiv)

Reinforcement learning involves decision making in dynamic and uncertain environments and constitutes a crucial element of artificial intelligence. In our previous work, we experimentally demonstrated that the ultrafast chaotic oscillatory dynamics of lasers can be used to solve the two-armed bandit problem efficiently, which requires decision making concerning a class of difficult trade-offs called the exploration-exploitation dilemma. However, only two selections were employed in that research; thus, the scalability of the laser-chaos-based reinforcement learning should be clarified. In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time-series. The experimental demonstrations in which bandit problems with up to 64 arms were successfully solved are presented in this report. Detailed analyses are also provided that include performance comparisons among laser chaos signals generated in different physical conditions, which coincide with the diffusivity inherent in the time series. This study paves the way for ultrafast reinforcement learning by taking advantage of the ultrahigh bandwidths of light wave and practical enabling technologies.