Results 21 to 30 of about 98,895 (273)
Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
Applied Sciences, 2021 Active tracking control is essential for UAVs to perform autonomous operations in GPS-denied environments. In the active tracking task, UAVs take high-dimensional raw images as input and execute motor actions to actively follow the dynamic target.
Wenlong Zhao +4 more
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Hierarchical Reinforcement Learning Method Based on Trajectory Information [PDF]
Jisuanji kexue, 2023 The option-based hierarchical reinforcement learning(O-HRL) algorithm has the characteristics of temporal abstraction,which can effectively deal with complex problems such as long-term temporal order and sparse rewards that are difficult to solve in ...
XU Yapeng, LIU Quan, LI Junwei
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Deep Reinforcement Learning from Hierarchical Preference Design
, 2023 Reward design is a fundamental, yet challenging aspect of reinforcement learning (RL). Researchers typically utilize feedback signals from the environment to handcraft a reward function, but this process is not always effective due to the varying scale and intricate dependencies of the feedback signals. This paper shows by exploiting certain structures,
Bukharin, Alexander +3 more
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Applied Sciences, 2023 This paper proposes a novel solution for using deep neural networks with reinforcement learning as a valid option in negotiating distributed hierarchical controller agents.
Oscar Aponte-Rengifo +2 more
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Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning [PDF]
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 Submitted to IROS ...
Chuck, Caleb +2 more
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A survey on automatic generation of medical imaging reports based on deep learning
BioMedical Engineering OnLine, 2023 Recent advances in deep learning have shown great potential for the automatic generation of medical imaging reports. Deep learning techniques, inspired by image captioning, have made significant progress in the field of diagnostic report generation. This
Ting Pang, Peigao Li, Lijie Zhao
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Nuclear Engineering and Technology, 2023 Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the ...
Hao Hu, Jiayue Wang, Ai Chen, Yang Liu
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Hierarchical Deep Reinforcement Learning for Continuous Action Control
IEEE Transactions on Neural Networks and Learning Systems, 2018 Robotic control in a continuous action space has long been a challenging topic. This is especially true when controlling robots to solve compound tasks, as both basic skills and compound skills need to be learned. In this paper, we propose a hierarchical deep reinforcement learning algorithm to learn basic skills and compound skills simultaneously.
Zhaoyang Yang +3 more
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Option-Critic Algorithm Based on Mutual Information Optimization [PDF]
Jisuanji kexueAs an important research content of hierarchical reinforcement learning,temporal abstraction allows hierarchical reinforcement learning agents to learn policies at different time scales,which can effectively solve the sparse reward problem that is ...
LI Junwei, LIU Quan, XU Yapeng
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HLifeRL: A hierarchical lifelong reinforcement learning framework
Journal of King Saud University: Computer and Information Sciences, 2022 Deep reinforcement learning research in a single-task environment has made remarkable achievements. However, it is often plagued by catastrophic forgetting, prohibitively low sample efficiency and lack of scalability problems when facing multi-task ...
Fan Ding, Fei Zhu
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