Results 11 to 20 of about 98,895 (273)
Deep Reinforcement Learning with Hierarchical Structures [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Hierarchical reinforcement learning (HRL), which enables control at multiple time scales, is a promising paradigm to solve challenging and long-horizon tasks. In this paper, we briefly introduce our work in bottom-up and top-down HRL and outline the directions for future work.
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Hierarchical Offset Object Detection Based on Human Visual Mechanism [PDF]
Jisuanji gongcheng, 2018 In order to solve the problem of low recall rate in object detection with the deep reinforcement learning method,on the basis of simulating human visual mechanism,a dynamic searching hierarchical offset method is proposed.It uses the idea of anchors ...
QIN Sheng,ZHANG Xiaolin,CHEN Lili,LI Jiamao
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Applied Sciences, 2023 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 ...
Rong Zhou, Zhisheng Zhang, Yuan Wang
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Intelligent Attack Path Discovery Based on Hierarchical Reinforcement Learning [PDF]
Jisuanji kexue, 2023 Intelligent attack path discovery is a key technology for automated penetration testing,but existing methods face the problems of exponential growth of state and action space and sparse rewards,which make the algorithm difficult to converge.To this end ...
ZENG Qingwei, ZHANG Guomin, XING Changyou, SONG Lihua
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Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022 Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class.
Zha, Daochen +5 more
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A Hierarchical Framework for Quadruped Robots Gait Planning Based on DDPG
Biomimetics, 2023 In recent years, significant progress has been made in employing reinforcement learning for controlling legged robots. However, a major challenge arises with quadruped robots due to their continuous states and vast action space, making optimal control ...
Yanbiao Li +4 more
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Dynamic Multichannel Sensing in Cognitive Radio: Hierarchical Reinforcement Learning
IEEE Access, 2021 Efficient use of spectral resources is critical in wireless networks and has been extensively studied in recent years. Dynamic spectrum access (DSA) is one of the key techniques on utilizing the spectral resources. Among them, reinforcement learning (RL)
Shuai Liu, Jiayun Wu, Jing He
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Hierarchical policy with deep-reinforcement learning for nonprehensile multiobject rearrangement
Biomimetic Intelligence and Robotics, 2022 Nonprehensile multiobject rearrangement is the robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping. It must consider how each object reaches the target and the order in which objects
Fan Bai +4 more
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Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 12 pages, 8 ...
Peng, Baolin +6 more
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A Survey on Visual Navigation for Artificial Agents With Deep Reinforcement Learning
IEEE Access, 2020 Visual navigation (vNavigation) is a key and fundamental technology for artificial agents' interaction with the environment to achieve advanced behaviors.
Fanyu Zeng, Chen Wang, Shuzhi Sam Ge
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