Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
Due to the powerful feature representation capability of deep learning and the effective policy learning capability of reinforcement learning (RL), deep reinforcement learning (DRL) has made remarkable achievements in a series of complex sequential decision-making problems. With the popularity of DRL in many single-agent tasks, its application in multi-agent systems is flourishing. Recently, multi-agent deep reinforcement learning (MADRL) has attracted increasing attention in the field of artificial intelligence, and the scalability and transferability have become one of the important issue
The subway station air conditioning system consumes a lot of energy, and traditional control methods cannot take into account the comfort and energy saving issues together, resulting in poor control effect. Moreover, the current subway station air conditioning system and the control air systems the control water system separately, which cannot guarantee the energy saving effect of the whole system. Therefore, this paper proposes an energy-saving control strategy for the system based on reinforcement learning. Firstly, this paper uses a neural network to establish an air system modification
In view of the insufficient accuracy of the orbital state prediction method based on the physical model in the space surveillance environment, and the insufficient reliability of the error compensation model based on machine learning, as well as the demand for uncertainty modeling in the SSA application, we reformulate the orbital state prediction error estimation problem as a probability prediction problem, and propose a method of using a gradient boosting machine to model the orbital state prediction error distribution. In order to quantify the uncertainty in the state error estimation, t
Image completion is an important research content in the field of digital image processing, and the completion of large area irregular missing images is a research hotspot in recent years. However, the existing image completion technology has some limitations. The method based on generative adversarial network ignores the edge structure information of the image, and it can"t restore the fine details. The method based on local discriminator can"t deal with the missing irregular image, and the completion task doesn"t conform to the actual application scene. Combined with the id
The sketch person re-identification requires to search for pedestrians with the same identity as the given sketch image in the color image gallery. Due to the difference of posture and viewpoint between the sketch image and the color image, the two images from two different modes often have different semantic information in the same spatial position, which leads to the lack of robustness of the extracted features. Previous studies focus on pedestrian feature extraction modal-invariant information, but ignore the issue of semantic misalignment between lean and different modals to features in