Log In Sign Up

Ultrafast photonic reinforcement learning based on laser chaos

by   Makoto Naruse, et al.

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.


page 27

page 28

page 29


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

Reinforcement learning involves decision making in dynamic and uncertain...

Theory of Acceleration of Decision Making by Correlated Times Sequences

Photonic accelerators have been intensively studied to provide enhanced ...

Parallel bandit architecture based on laser chaos for reinforcement learning

Accelerating artificial intelligence by photonics is an active field of ...

Controlling chaotic itinerancy in laser dynamics for reinforcement learning

Photonic artificial intelligence has attracted considerable interest in ...

Category theoretic foundation of single-photon-based decision making

Decision making is a vital function in the age of machine learning and a...

Lotka-Volterra competition mechanism embedded in a decision-making method

Decision making is a fundamental capability of living organisms, and has...

Establishing Meta-Decision-Making for AI: An Ontology of Relevance, Representation and Reasoning

We propose an ontology of building decision-making systems, with the aim...