Ghost imaging using deep learning (GiDL) is a kind of computational quantum imaging method devised to improve the imaging efficiency. However, among most proposals of GIDL so far, the same set of random patterns were used in both the training and test set, leading to a decrease of the generalization ability of networks. Thus, the GIDL technique can only reconstruct the profile of the image of the object, losing most of the details. Here we optimize the simulation algorithm of ghost imaging (GI) by introducing the concept of "batch" into the pre-processing stage. It can significantly reduce the data acquisition time and create reliable simulation data. The generalization ability of GIDL has been appreciably enhanced. Furthermore, we develop a residual-based framework for the GI system, namely the double residual U-Net (DRU-Net). The imaging quality of GI has been tripled in the evaluation of the structural similarity index by our proposed DRU-Net. Ghost imaging (GI) is an unconventional imaging method compared with traditional optical imaging methods. It was first proposed as a quantum entangled phenomenon by making use of the entangled two-photon light source generated by the spontaneous parameter down-conversion (SPDC) process 1. However, GI was demonstrated to be accomplished by classical incoherent light source soon later 2. It led to controversy focus on whether the quantum entangled is necessary for the GI system. The GI system contains two distributed light beams, which are the test beam and the reference beam. In the test beam, the light illuminates the object directly and then be collected into a bucket measurement. In the reference beam, the light travels freely to a high-resolution detector without interacting with the object. The image can then be reconstructed through the correlation measurement between the two light beam signals 3. In terms of computational ghost imaging (CGI), the reference beam becomes unnecessary as one can obtain the image by calculating the correlation between the test beam intensity and the knowledge of the random patterns displayed on the spatial light modulator (SLM) 4-6. CGI usually needs a large number of random patterns to avoid noise disturbance to achieve images of high resolution. This requirement leads to a long data acquisition time, which is the main issue preventing CGI from practical use. Improved correlation method such as normalized ghost imaging 7 and differential ghost imaging 8 was later proposed to increase imaging efficiency. Notably, the Gerchberg-Saxton algorithm and compressive sensing ghost imaging (CSGI) regard GI as an optimization problem. In 9 , the Gerchberg-Saxton-like technique takes the integral property of Fourier transform into full consideration to provide a different perspective of image reconstruction of GI. CSGI can reduce measurement times and boost imaging quality 10-14. Recently, deep learning (DL) has achieved widespread use in various fields, such as image denoising, image inpainting, natural language processing, to name a few 15. DL h...

In this paper, we study the problem of networked multi-agent reinforcement learning (MARL), where a number of agents are deployed as a partially connected network and each interacts only with nearby agents. Networked MARL requires all agents make decision in a decentralized manner to optimize a global objective with restricted communication between neighbors over the network. Inspired by the fact that sharing plays a key role in human's learning of cooperation, we propose LToS, a hierarchically decentralized MARL framework that enables agents to learn to dynamically share reward with neighbors so as to encourage agents to cooperate on the global objective. For each agent, the high-level policy learns how to share reward with neighbors to decompose the global objective, while the low-level policy learns to optimize local objective induced by the high-level policies in the neighborhood. The two policies form a bi-level optimization and learn alternately. We empirically demonstrate that LToS outperforms existing methods in both social dilemma and networked MARL scenario.Preprint.