# Ultrafast photonic reinforcement learning based on laser chaos

arXiv: https://arxiv.org/abs/1704.04379 · HTML: https://mlsanity.com/paper/1704.04379/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2017.

## Built on by (most cited first, 13 of 13)

- [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)
- [Contextual Probability and Operational Minimality in Tug-of-War Decision Making](https://mlsanity.com/paper/2601.10034/) (2026)
- [A Balance for Fairness: Fair Distribution Utilising Physics in Games of Characteristic Function Form](https://mlsanity.com/paper/2101.11496/) (2021)
- [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)
- [Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network](https://mlsanity.com/paper/2312.02537/) (2023)
- [Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network](https://mlsanity.com/paper/2407.09124/) (2024)
- [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)
- [Constraint-Enhanced Physical Search through Correlation Matching](https://mlsanity.com/paper/2606.03554/) (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 one important element of artificial intelligence (AI). In this paper, we experimentally demonstrate that the ultrafast chaotic oscillatory dynamics of lasers efficiently solve the multi-armed bandit problem (MAB), which requires decision making concerning a class of difficult trade-offs called the exploration-exploitation dilemma. To solve the MAB, a certain degree of randomness is required for exploration purposes. However, pseudo-random numbers generated using conventional electronic circuitry encounter severe limitations in terms of their data rate and the quality of randomness due to their algorithmic foundations. We generate laser chaos signals using a semiconductor laser sampled at a maximum rate of 100 GSample/s, and combine it with a simple decision-making principle called tug-of-war with a variable threshold, to ensure ultrafast, adaptive and accurate decision making at a maximum adaptation speed of 1 GHz. We found that decision-making performance was maximized with an optimal sampling interval, and we highlight the exact coincidence between the negative autocorrelation inherent in laser chaos and decision-making performance. This study paves the way for a new realm of ultrafast photonics in the age of AI, where the ultrahigh bandwidth of photons can provide new value.