Decision-making and control with diffractive optical networks

• URL: http://arxiv.org/abs/2212.11278v3
• Date: Thu, 21 Sep 2023 13:34:54 GMT
• Title: Decision-making and control with diffractive optical networks
• Authors: Jumin Qiu, Shuyuan Xiao, Lujun Huang, Andrey Miroshnichenko, Dejian Zhang, Tingting Liu, Tianbao Yu
• Abstract summary: 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. Here, we propose using deep reinforcement learning to implement diffractive optical networks that imitate human-level decision-making and control capability.
• Score: 1.3356127774729136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Optical training of large-scale Transformers and deep neural networks with direct feedback alignment [48.90869997343841]
  We experimentally implement a versatile and scalable training algorithm, called direct feedback alignment, on a hybrid electronic-photonic platform. An optical processing unit performs large-scale random matrix multiplications, which is the central operation of this algorithm, at speeds up to 1500 TeraOps. We study the compute scaling of our hybrid optical approach, and demonstrate a potential advantage for ultra-deep and wide neural networks.
  arXiv  Detail & Related papers  (2024-09-01T12:48:47Z)
• Coherence Awareness in Diffractive Neural Networks [21.264497139730473]
  We show that in diffractive networks the degree of spatial coherence has a dramatic effect. In particular, we show that when the spatial coherence length on the object is comparable to the minimal feature size preserved by the optical system, neither the incoherent nor the coherent extremes serve as acceptable approximations.
  arXiv  Detail & Related papers  (2024-08-13T07:19:40Z)
• Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
  Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
  arXiv  Detail & Related papers  (2024-04-29T04:51:05Z)
• HGFF: A Deep Reinforcement Learning Framework for Lifetime Maximization in Wireless Sensor Networks [5.4894758104028245]
  We propose a new framework combining heterogeneous graph neural network with deep reinforcement learning to automatically construct the movement path of the sink. We design ten types of static and dynamic maps to simulate different wireless sensor networks in the real world. Our approach consistently outperforms the existing methods on all types of maps.
  arXiv  Detail & Related papers  (2024-04-11T13:09:11Z)
• Genetically programmable optical random neural networks [0.0]
  We demonstrate a genetically programmable yet simple optical neural network to achieve high performances with optical random projection. By genetically programming the orientation of the scattering medium which acts as a random projection kernel, our novel technique finds an optimum kernel and improves its initial test accuracies 7-22%. Our optical computing method presents a promising approach to achieve high performance in optical neural networks with a simple and scalable design.
  arXiv  Detail & Related papers  (2024-03-19T06:55:59Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing [52.50284630866713]
  Existing systems often require hand-engineered components for state estimation, planning, and control. This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies.
  arXiv  Detail & Related papers  (2022-10-26T19:03:17Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Physical deep learning based on optimal control of dynamical systems [0.0]
  In this study, we perform pattern recognition based on the optimal control of continuous-time dynamical systems. As a key example, we apply the dynamics-based recognition approach to an optoelectronic delay system. This is in contrast to conventional multilayer neural networks, which require a large number of weight parameters to be trained.
  arXiv  Detail & Related papers  (2020-12-16T06:38:01Z)
• Compatible Learning for Deep Photonic Neural Network [0.0]
  Photonic neural network has a significant potential for prediction-oriented tasks. We develop a compatible learning protocol in complex space, of which nonlinear activation could be selected efficiently.
  arXiv  Detail & Related papers  (2020-03-14T13:21:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.