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.

本文介绍了一种基于光子技术和强化学习的主动学习机制的设计方案，包括 SARSA, Q-learning 和 projective simulation 等算法。该方案可实现抽象和概括机制，并且具有可扩展性和可移植性。