  The ultimate goal of artificial intelligence is to mimic the human brain to
perform decision-making and control directly from high-dimensional sensory
input. Diffractive optical networks provide a promising solution for
implementing artificial intelligence with high-speed and low-power consumption.
Most of the reported diffractive optical networks focus on single or multiple
tasks that do not involve environmental interaction, such as object recognition
and image classification. In contrast, the networks capable of performing
decision-making and control have not yet been developed to our knowledge. Here,
we propose using deep reinforcement learning to implement diffractive optical
networks that imitate human-level decision-making and control capability. Such
networks taking advantage of a residual architecture, allow for finding optimal
control policies through interaction with the environment and can be readily
implemented with existing optical devices. The superior performance of these
networks is verified by engaging three types of classic games, Tic-Tac-Toe,
Super Mario Bros., and Car Racing. Finally, we present an experimental
demonstration of playing Tic-Tac-Toe by leveraging diffractive optical networks
based on a spatial light modulator. Our work represents a solid step forward in
advancing diffractive optical networks, which promises a fundamental shift from
the target-driven control of a pre-designed state for simple recognition or
classification tasks to the high-level sensory capability of artificial
intelligence. It may find exciting applications in autonomous driving,
intelligent robots, and intelligent manufacturing.
