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Xiaoping Zhang, Yitong Wu, Huijiang Wang, Fumiya Iida and Li Wang
Animals have evolved to adapt to complex and uncertain environments, acquiring locomotion skills for diverse surroundings. To endow a robot?s animal-like locomotion ability, in this paper, we propose a learning algorithm for quadruped robots based on dee...
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Abiodun Abiola, Francisca Segura Manzano and José Manuel Andújar
Hydrogen provides a clean source of energy that can be produced with the aid of electrolysers. For electrolysers to operate cost-effectively and safely, it is necessary to define an appropriate maintenance strategy. Predictive maintenance is one of such ...
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Zhuang Wang, Weijun Pan, Hui Li, Xuan Wang and Qinghai Zuo
Deep reinforcement learning (DRL) has been widely adopted recently for its ability to solve decision-making problems that were previously out of reach due to a combination of nonlinear and high dimensionality. In the last few years, it has spread in the ...
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Wongwan Jung and Daejun Chang
This study proposed a deep reinforcement learning-based energy management strategy (DRL-EMS) that can be applied to a hybrid electric ship propulsion system (HSPS) integrating liquid hydrogen (LH2) fuel gas supply system (FGSS), proton-exchange membrane ...
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Rafael Beltrame,Marília Lazarotto,Clovis Roberto Haselein,Elio José Santini,Paulo Renato Schneider,Aurélio Mendes Aguiar
Pág. 343 - 351
http://dx.doi.org/10.5902/198050985741O presente estudo foi desenvolvido com o objetivo de determinar as deformações residuais longitudinais (DRL), decorrentes das tensões de crescimento em árvores em pé e vivas de diferentes clones de Eucalyptus spp. Pa...
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Xu Feng, Mengyang He, Lei Zhuang, Yanrui Song and Rumeng Peng
SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneo...
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Alamir Labib Awad, Saleh Mesbah Elkaffas and Mohammed Waleed Fakhr
Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has int...
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Xi Lyu, Yushan Sun, Lifeng Wang, Jiehui Tan and Liwen Zhang
This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-bas...
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Youpeng Tu, Haiming Chen, Linjie Yan and Xinyan Zhou
In IoT (Internet of Things) edge computing, task offloading can lead to additional transmission delays and transmission energy consumption. To reduce the cost of resources required for task offloading and improve the utilization of server resources, in t...
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Hung Tuan Trinh, Sang-Hoon Bae and Duy Quang Tran
The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructure...
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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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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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Eduardo Navarrete,Carolina Valenzuela,Leif Nutto
Pág. 901 - 912
El lugar de estudio correspondió a un rodal de 12 años de edad, el cual fue intervenido a los 2 años y medio de establecido, donde se evaluaron distintas intensidades de poda (0% y 60% de la altura total) y densidades de raleo (1.600 árb·ha-1 (sin raleo)...
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Rafael de Avila Delucis,André Luiz Missio,Rafael Beltrame,Darci Alberto Gatto
Pág. DOI: 10.12 - 6830.v05n0
O presente trabalho visou analisar as tensões de crescimento da madeira de acácia negra (Acacia mearnsii De Wild) com vistas a relaciona-las com propriedades dendrométricas das árvores e físicas da madeira. Para isso, foram selecionadas 30 árvores de set...
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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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Reinis Cimurs, Vilnis Turkovs, Martins Banis and Aleksandrs Korsunovs
For mobile cleaning robot navigation, it is crucial to not only base the motion decisions on the ego agent?s capabilities but also to take into account other agents in the shared environment. Therefore, in this paper, we propose a deep reinforcement lear...
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Hao Yuan and Dongxu Li
This paper deals with the guidance problem of close approaching small celestial bodies while autonomously navigating with an optical camera. A combination of a deep reinforcement learning (DRL)-based guidance method and a ?Stop-and-Go? (SaG) strategy is ...
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Jiqing Du, Dan Zhou, Wei Wang and Sachiyo Arai
The Deep Reinforcement Learning (DRL) algorithm is an optimal control method with generalization capacity for complex nonlinear coupled systems. However, the DRL agent maintains control command saturation and response overshoot to achieve the fastest res...
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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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Xiaoping Zhang, Yuanpeng Zheng, Li Wang, Arsen Abdulali and Fumiya Iida
Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces...
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