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Beipo Su, Yongfeng Ju and Liang Dai
Video application is a research hotspot in cooperative vehicle-infrastructure systems (CVIS) which is greatly related to traffic safety and the quality of user experience. Dealing with large datasets of feedback from complex environments is a challenge w...
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Wenzel Pilar von Pilchau, Anthony Stein and Jörg Hähner
State-of-the-art Deep Reinforcement Learning Algorithms such as DQN and DDPG use the concept of a replay buffer called Experience Replay. The default usage contains only the experiences that have been gathered over the runtime. We propose a method called...
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Yubing Mao, Farong Gao, Qizhong Zhang and Zhangyi Yang
This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as the controller of an AUV. Based on the generative adversarial imitation (GAIL) algorithm combined with a multi-agent, a multi-age...
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Honghu Xue, Benedikt Hein, Mohamed Bakr, Georg Schildbach, Bengt Abel and Elmar Rueckert
We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. In our approach, an automatic guided vehicle is equipped with two LiDAR sensors and one frontal RGB camera and learns to perform a targeted...
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Fidel Aznar, Mar Pujol and Ramón Rizo
This article presents a macroscopic swarm foraging behavior obtained using deep reinforcement learning. The selected behavior is a complex task in which a group of simple agents must be directed towards an object to move it to a target position without t...
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Santosh Kumar Sahu, Anil Mokhade and Neeraj Dhanraj Bokde
Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as w...
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Rong Zhou, Zhisheng Zhang and Yuan Wang
Deep reinforcement learning is one of the research hotspots in artificial intelligence and has been successfully applied in many research areas; however, the low training efficiency and high demand for samples are problems that limit the application. Ins...
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Yuxing Wang, Nan Liu, Zhiwen Pan and Xiaohu You
Network slicing is a key technology for 5G networks, which divides the traditional physical network into multiple independent logical networks to meet the diverse requirements of end-users. This paper focuses on the resource allocation problem in the sce...
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Yang Shi, Zhenbo Wang, Tim J. LaClair, Chieh (Ross) Wang, Yunli Shao and Jinghui Yuan
The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce co...
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Hao Wang, Jinan Zhu and Bao Gu
In the modern world, the extremely rapid growth of traffic demand has become a major problem for urban traffic development. Continuous optimization of signal control systems is an important way to relieve traffic pressure in cities. In recent years, with...
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Jingjing Zhang, Yanlong Liu and Weidong Zhou
Adaptive sampling of the marine environment may improve the accuracy of marine numerical prediction models. This study considered adaptive sampling path optimization for a three-dimensional (3D) marine observation platform, leading to a path-planning str...
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Benjamin Warnke, Stefan Fischer and Sven Groppe
Due to increasing digitization, the amount of data in the Internet of Things (IoT) is constantly increasing. In order to be able to process queries efficiently, strategies must, therefore, be found to reduce the transmitted data as much as possible. SPAR...
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Tongyang Xu, Yuan Liu, Zhaotai Ma, Yiqiang Huang and Peng Liu
As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogen...
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Pedro Almeida, Vitor Carvalho and Alberto Simões
Reinforcement Learning is one of the many machine learning paradigms. With no labelled data, it is concerned with balancing the exploration and exploitation of an environment with one or more agents present in it. Recently, many breakthroughs have been m...
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Suleiman Abahussein, Dayong Ye, Congcong Zhu, Zishuo Cheng, Umer Siddique and Sheng Shen
Online food delivery services today are considered an essential service that gets significant attention worldwide. Many companies and individuals are involved in this field as it offers good income and numerous jobs to the community. In this research, we...
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Yazhi Liu, Dongyu Wei, Chunyang Zhang and Wei Li
In QoE fairness optimization of multiple video streams, a distributed video stream fairness scheduling strategy based on federated deep reinforcement learning is designed to address the problem of low bandwidth utilization due to unfair bandwidth allocat...
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Lizhen Wu, Chang Wang, Pengpeng Zhang and Changyun Wei
Autonomous Unmanned Aerial Vehicle (UAV) landing remains a challenge in uncertain environments, e.g., landing on a mobile ground platform such as an Unmanned Ground Vehicle (UGV) without knowing its motion dynamics. A traditional PID (Proportional, Integ...
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Shuailong Li, Wei Zhang, Yuquan Leng and Xiaohui Wang
Environmental information plays an important role in deep reinforcement learning (DRL). However, many algorithms do not pay much attention to environmental information. In multi-agent reinforcement learning decision-making, because agents need to make de...
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Yan Zeng, Jiyang Wu, Jilin Zhang, Yongjian Ren and Yunquan Zhang
Deep learning, with increasingly large datasets and complex neural networks, is widely used in computer vision and natural language processing. A resulting trend is to split and train large-scale neural network models across multiple devices in parallel,...
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Qiuxuan Wu, Yueqin Gu, Yancheng Li, Botao Zhang, Sergey A. Chepinskiy, Jian Wang, Anton A. Zhilenkov, Aleksandr Y. Krasnov and Sergei Chernyi
The cable-driven soft arm is mostly made of soft material; it is difficult to control because of the material characteristics, so the traditional robot arm modeling and control methods cannot be directly applied to the soft robot arm. In this paper, we c...
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