Photonic architecture for reinforcement learning
This work addresses the integration of photonic technologies with AI for more efficient computing, though it is incremental as it builds on existing algorithms and simulations without experimental validation.
The authors proposed a photonic hardware architecture for reinforcement learning algorithms like SARSA and Q-learning, demonstrating through numerical simulations that it tolerates experimental noise and enables abstraction and generalization mechanisms.
The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in portable systems appears to be within the reach of near-term technology.