The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. All-optical diffractive neural networks provide a promising solution for realizing artificial intelligence with high-speed and low-power consumption. To date, most of the reported diffractive neural networks focus on single or multiple tasks that do not involve interaction with the environment, such as object recognition and image classification, while the networks that can perform decision-making and control, to our knowledge, have not been developed yet. Here, we propose to use deep reinforcement learning to realize diffractive neural networks that enable imitating the human-level capability of decision-making and control. Such networks allow for finding optimal control policies through interaction with the environment and can be readily realized with the dielectric metasurfaces. The superior performances of these networks are verified by engaging three types of classic games, Tic-Tac-Toe, Super Mario Bros., and Car Racing, and achieving the same or even higher levels comparable to human players. Our work represents a solid step of advancement in diffractive neural 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.
翻译:人工智能的最终目标是模仿人类大脑,直接从高维感官输入中进行决策和控制。全光 diffractive神经网络为实现高速和低电消耗的人工智能提供了一个有希望的解决方案。迄今为止,大多数报告的 diffractive神经网络侧重于单一或多重任务,这些任务并不涉及与环境的互动,例如物体识别和图像分类,而能够进行决策和控制的网络尚未开发到我们的知识。在这里,我们提议利用深度强化学习,实现能模仿人类决策和控制能力的硬性神经网络。这些网络通过与环境的互动,可以找到最佳的控制政策,并且可以很容易地实现。这些网络的优异性表现是通过三种类型的经典游戏(Tic-Tac-Toe, Super Mario Bros.)和Car Racing,以及达到与人类玩家相同的甚至更高的水平。我们的工作代表着在智能智能智能网络的提升上迈出了坚实的一步,通过与环境互动互动,或者与电动的超动性机械智能网络的升级能力,可以让它从一个简单的智能智能智能水平上找到一个根本的机器人控制能力。