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Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez and Susan A. Murphy
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring th...
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Ali Barzegar and Deok-Jin Lee
This research study presents a new adaptive attitude and altitude controller for an aerial robot. The proposed controlling approach employs a reinforcement learning-based algorithm to actively estimate the controller parameters of the aerial robot. In de...
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Dugan Um, Prasad Nethala and Hocheol Shin
In this paper, a hierarchical reinforcement learning (HRL) architecture, namely a ?Hierarchical Deep Deterministic Policy Gradient (HDDPG)? has been proposed and studied. A HDDPG utilizes manager and worker formation similar to other HRL structures. Howe...
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Ajmery Sultana and Xavier Fernando
Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant ena...
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Arun Kumar Sangaiah, Amir Javadpour, Chung-Chian Hsu, Anandakumar Haldorai and Ahmad Zeynivand
Vehicular Ad Hoc Network (VANETs) need methods to control traffic caused by a high volume of traffic during day and night, the interaction of vehicles, and pedestrians, vehicle collisions, increasing travel delays, and energy issues. Routing is one of th...
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Teng Liu, Yuan Zou, Dexing Liu and Fengchun Sun
This paper presents a reinforcement learning (RL)?based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the exp...
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Fengyuan Yin, Xiaoming Yuan, Zhiao Ma and Xinyu Xu
Permanent magnet synchronous motor (PMSM) drive systems are commonly utilized in mobile electric drive systems due to their high efficiency, high power density, and low maintenance cost. To reduce the tracking error of the permanent magnet synchronous mo...
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Wenting Li, Xiuhui Zhang, Yunfeng Dong, Yan Lin and Hongjue Li
Multi-stage launch vehicles are currently the primary tool for humans to reach extraterrestrial space. The technology of recovering and reusing rockets can effectively shorten rocket launch cycles and reduce space launch costs. With the development of de...
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Smita Mahajan, Shruti Patil, Moinuddin Bhavnagri, Rashmi Singh, Kshitiz Kalra, Bhumika Saini, Ketan Kotecha and Jatinderkumar Saini
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN). This is a first step towards the greater goal of developing fast-learning and robust RL age...
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Gleice Kelly Barbosa Souza, Samara Oliveira Silva Santos, André Luiz Carvalho Ottoni, Marcos Santos Oliveira, Daniela Carine Ramires Oliveira and Erivelton Geraldo Nepomuceno
Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requir...
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Shui Jiang, Yanning Ge, Xu Yang, Wencheng Yang and Hui Cui
Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to navigate and make decisions efficiently and intelligently within complex and dynamic surroundings. Despite its significance, RL is hampered by inherent limitations su...
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Siyuan Yang, Mondher Bouazizi, Tomoaki Ohtsuki, Yohei Shibata, Wataru Takabatake, Kenji Hoshino and Atsushi Nagate
In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random mo...
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Yaxuan Wang, Zhixin Zeng, Qiushan Li and Yingrui Deng
Urban-safety perception is crucial for urban planning and pedestrian street preference studies. With the development of deep learning and the availability of high-resolution street images, the use of artificial intelligence methods to deal with urban-saf...
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Rafet Durgut, Mehmet Emin Aydin and Abdur Rakib
In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a paramet...
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Martin Greguric, Miroslav Vujic, Charalampos Alexopoulos and Mladen Miletic
Persistent congestions which are varying in strength and duration in the dense traffic networks are the most prominent obstacle towards sustainable mobility. Those types of congestions cannot be adequately resolved by the traditional Adaptive Traffic Sig...
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Marta Ribeiro, Joost Ellerbroek and Jacco Hoekstra
Future high traffic densities with drone operations are expected to exceed the number of aircraft that current air traffic control procedures can control simultaneously. Despite extensive research on geometric CR methods, at higher densities, their perfo...
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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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Nan Ma, Ziyi Wang, Zeyu Ba, Xinran Li, Ning Yang, Xinyi Yang and Haifeng Zhang
Crude oil resource scheduling is one of the critical issues upstream in the crude oil industry chain. It aims to reduce transportation and inventory costs and avoid alerts of inventory limit violations by formulating reasonable crude oil transportation a...
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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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Raphael C. Engelhardt, Marc Oedingen, Moritz Lange, Laurenz Wiskott and Wolfgang Konen
The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DTs) as simple and transparent surrogate models for opaque d...
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