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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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Zixiao Zhu, Lichuan Zhang, Lu Liu, Dongwei Wu, Shuchang Bai, Ranzhen Ren and Wenlong Geng
Positioning errors introduced by low-precision navigation devices can affect the overall accuracy of a positioning system. To address this issue, this paper proposes a master-slave multi-AUV collaborative navigation method based on hierarchical reinforce...
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Adrian Millea and Abbas Edalat
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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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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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Weiyan Ren, Dapeng Han and Zhaokui Wang
When a lunar assisted robot helps an astronaut turn over or transports the astronaut from the ground, the trajectory of the robot?s dual arms should be automatically planned according to the unstructured environment on the lunar surface. In this paper, a...
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Ruijun Hu and Yulin Zhang
The global path planning of planetary surface rovers is crucial for optimizing exploration benefits and system safety. For the cases of long-range roving or obstacle constraints that are time-varied, there is an urgent need to improve the computational e...
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Jonathan Ponniah and Or D. Dantsker
A system is considered in which agents (UAVs) must cooperatively discover interest-points (i.e., burning trees, geographical features) evolving over a grid. The objective is to locate as many interest-points as possible in the shortest possible time fram...
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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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Weiqiang Wang, Liwen Huang, Kezhong Liu, Xiaolie Wu and Jingyao Wang
It is crucial to develop a COLREGs-compliant intelligent collision avoidance system for the safety of unmanned ships during navigation. This paper proposes a collision avoidance decision approach based on the deep reinforcement learning method. A modifie...
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