Results 251 to 260 of about 404,230 (315)

Skill-Based Hierarchical Reinforcement Learning for Target Visual Navigation

IEEE transactions on multimedia, 2023
Target visual navigation aims at controlling the agent to find a target object based on a monocular visual RGB image in each step. It is crucial for the agent to adapt to new environments.
Shuo Wang, Zhihao Wu, X. Hu, Youfang Lin, K. Lv   +4 more
semanticscholar   +1 more source

A Hierarchical Reinforcement Learning Algorithm Based on Attention Mechanism for UAV Autonomous Navigation

IEEE transactions on intelligent transportation systems (Print), 2023
Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. Meanwhile, UAV’s ability of autonomous navigation and obstacle avoidance becomes more and more critical.
Zun Liu, Yuanqiang Cao, Jianyong Chen, Jianqiang Li   +3 more
semanticscholar   +1 more source

Intelligent problem-solving as integrated hierarchical reinforcement learning

Nature Machine Intelligence, 2022
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms.
Manfred Eppe, Christian Gumbsch, Matthias Kerzel, Phuong D. H. Nguyen, Martin Volker Butz, Stefan Wermter   +5 more
semanticscholar   +1 more source

Hierarchical Reinforcement Learning for Conversational Recommendation With Knowledge Graph Reasoning and Heterogeneous Questions

IEEE Transactions on Services Computing, 2023
User interaction history with items is used to infer user preferences in conventional recommendation systems. Among these, conversational recommendation systems (CRSs), which provide effective recommendations based on a framework combining ...
Yao-Chun Yang, Chiao-Ting Chen, Tzu-Yu Lu, Szu-Hao Huang   +3 more
semanticscholar   +1 more source

Rethinking Decision Transformer via Hierarchical Reinforcement Learning

International Conference on Machine Learning, 2023
Decision Transformer (DT) is an innovative algorithm leveraging recent advances of the transformer architecture in reinforcement learning (RL). However, a notable limitation of DT is its reliance on recalling trajectories from datasets, losing the ...
Yi Ma, Chenjun Xiao, Hebin Liang, Jianye Hao   +3 more
semanticscholar   +1 more source

When Moving Target Defense Meets Attack Prediction in Digital Twins: A Convolutional and Hierarchical Reinforcement Learning Approach

IEEE Journal on Selected Areas in Communications, 2023
With rapid development of emerging technologies for Internet of Things (IoT), digital twins (DT) have been proposed to support a wide variety of applications.
Zhang Tao, Changqiao Xu, Yibo Lian, Haijiang Tian, Jiawen Kang, Xiaohui Kuang, Dusist Niyato   +6 more
semanticscholar   +1 more source

Hierarchical Reinforcement Learning for Dynamic Autonomous Vehicle Navigation at Intelligent Intersections

Knowledge Discovery and Data Mining, 2023
Recent years have witnessed the rapid development of the Cooperative Vehicle Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and autonomous vehicles (AVs) can share information among each other and work ...
Qian Sun, Le Zhang, Huan Yu, Weijiao Zhang, Yu Mei, Hui Xiong   +5 more
semanticscholar   +1 more source

Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation

International Conference on Information and Knowledge Management, 2023
Goal-oriented Learning path recommendation aims to recommend learning items (concepts or exercises) step-by-step to a learner to promote the mastery level of her specific learning goals.
Qingyao Li, Wei Xia, Li'ang Yin, Jian Shen, Renting Rui, Weinan Zhang, Xianyu Chen, Ruiming Tang, Yong Yu   +8 more
semanticscholar   +1 more source

Vision-Based Autonomous Driving: A Hierarchical Reinforcement Learning Approach

IEEE Transactions on Vehicular Technology, 2023
Human drivers have excellent perception and reaction abilities in complex environments such as dangerous highways, busy intersections, and harsh weather conditions.
Jiao Wang, Haoyi Sun, Can Zhu
semanticscholar   +1 more source

Hierarchical Bayesian Inverse Reinforcement Learning

IEEE Transactions on Cybernetics, 2015
Inverse reinforcement learning (IRL) is the problem of inferring the underlying reward function from the expert's behavior data. The difficulty in IRL mainly arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behavior data as optimal.
Choi, JD Choi, Jae-Deug, Kim, KE Kim, Kee-Eung   +1 more
openaire   +3 more sources