Results 41 to 50 of about 387,064 (296)

Reinforcement Learning for Rate-Distortion Optimized Hierarchical Prediction Structure

IEEE Access, 2023
Video coding standards use a prediction structure to arrange video frames and exploit temporal correlations. In this aspect, it is crucial to resolve complicated temporal dependencies among frames to improve coding efficiency because the coding of a ...
Jung-Kyung Lee, Nayoung Kim, Je-Won Kang
doaj   +1 more source

Learning Representations in Model-Free Hierarchical Reinforcement Learning

, 2019
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale applications involving huge state spaces and sparse delayed reward feedback.
Noelle, David C., Rafati, Jacob
core   +1 more source

Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments

Actuators
Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process.
Liwei Mei, Pengjie Xu
doaj   +1 more source

Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning

Journal of Advanced Transportation, 2018
Reinforcement learning method has a self-learning ability in complex multidimensional space because it does not need accurate mathematical model and due to the low requirement for prior knowledge of the environment.
Yizhe Wang, Xiaoguang Yang, Yangdong Liu, Hailun Liang   +3 more
doaj   +1 more source

Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks

Journal of Intelligence and Information Systems, 2022
Penetration testing (PT) is a method for assessing and evaluating the security of digital assets by planning, generating, and executing possible attacks that aim to discover and exploit vulnerabilities.
Mohamed Chahine Ghanem, Thomas M. Chen, E. Nepomuceno   +2 more
semanticscholar   +1 more source

Deep Reinforcement Learning Agent for Negotiation in Multi-Agent Cooperative Distributed Predictive Control

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, Pastora Vega, Mario Francisco   +2 more
doaj   +1 more source

Hierarchical Reinforcement Learning for Integrated Recommendation

AAAI Conference on Artificial Intelligence, 2021
Integrated recommendation aims to jointly recommend heterogeneous items in the main feed from different sources via multiple channels, which needs to capture user preferences on both item and channel levels.
Ruobing Xie, Shaoliang Zhang, Rui Wang, Feng Xia, Leyu Lin   +4 more
semanticscholar   +1 more source

Diversity-Driven Extensible Hierarchical Reinforcement Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. However, HRL with multiple levels is
Song, Y, Wang, J, Lukasiewicz, T, Zhenghua, X, Xu, M   +4 more
openaire   +4 more sources

Hierarchical Reinforcement Learning With Universal Policies for Multistep Robotic Manipulation

IEEE Transactions on Neural Networks and Learning Systems, 2021
Multistep tasks, such as block stacking or parts (dis)assembly, are complex for autonomous robotic manipulation. A robotic system for such tasks would need to hierarchically combine motion control at a lower level and symbolic planning at a higher level.
Xintong Yang, Ze Ji, Jing Wu, Yu-Kun Lai, Changyun Wei, Guoliang Liu, R. Setchi   +6 more
semanticscholar   +1 more source

Crucial parameters for precise copy number variation detection in formalin‐fixed paraffin‐embedded solid cancer samples

Molecular Oncology, EarlyView.
This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris, Vasiliki Siozopoulou, Léon C van Kempen, Anne Sieben, Ella Roelant, Stig Hellemans, Elyne Backx, Laure Sorber, Koen De Winne, Senada Koljenović, Karen Zwaenepoel   +10 more
wiley   +1 more source