Results 121 to 130 of about 387,064 (296)

Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control with Spatio-Temporal Attention Mechanism

Applied Sciences
Traffic congestion in large-scale road networks significantly impacts urban sustainability. Traditional traffic signal control methods lack adaptability to dynamic traffic conditions. Recently, deep reinforcement learning (DRL) has emerged as a promising
Wenzhe Jia, Mingyu Ji
doaj   +1 more source

Liquid Crystalline Elastomers in Soft Robotics: Assessing Promise and Limitations

Advanced Robotics Research, EarlyView.
Liquid crystalline elastomers (LCEs) are programmable soft materials that undergo large, anisotropic deformation in response to external stimuli. Their molecular alignment encodes directional actuation in a monolithic structure, making them long‐standing candidates for soft robotic systems.
Justin M. Speregen, Timothy J. White
wiley   +1 more source

Decoding the Cellular Heterogeneity and Malignant Progression of Human Penile Squamous Cell Carcinoma by Single‐Cell RNA Sequencing

Advanced Science, EarlyView.
Single‐cell transcriptomic and functional analyses identify SEMA3C as a key regulator of tumor progression and tumor microenvironment remodeling in penile squamous cell carcinoma. SEMA3C promotes epithelial–mesenchymal transition, tumor growth, and invasion while shaping an immunosuppressive microenvironment, highlighting its potential as a prognostic ...
Xiheng Hu, Wensheng Shi, Liang Dong, Lingjuan Huang, Xiyuan Zhang, Yiting Feng, Jie Sun, Lanlan Liu, Teng Liu, Jun Fu, Bowen Zhong, Qihao Leng, Xiaohua Wu, Minfeng Chen, Lingfang Li, Yuan Li, Xin Jin, Long Wang, Jian Cao, Xin Li, Mingzhu Yin, Xiang Chen   +21 more
wiley   +1 more source

Representation learning for hierarchical reinforcement learning

, 2023
Hierarchical Reinforcement Learning (HRL) has the potential to simplify the solution of environments with long horizons and sparse rewards. The idea behind HRL is to decompose a complex decision-making problem into smaller, manageable sub-problems, allowing an agent to learn more efficiently and effectively.
openaire   +1 more source

NDST3‐Induced Epigenetic Reprogramming Reverses Neurodegeneration in Parkinson's Disease

Advanced Science, EarlyView.
NDST3‐mediated epigenetic reprogramming revitalizes neuronal circuits in the substantia nigra and striatum to halt dopaminergic neuron degeneration and restore motor function in Parkinson's disease models. This strategy promotes neuronal maintenance and functional recovery, highlighting NDST3's therapeutic potential in neurodegenerative disorders ...
Yujung Chang, Yongwoo Na, Hyeonjoo Im, Garam Yang, Seungseon Yang, Hyun Soo Shim, Chunggoo Kim, GyeungYun Kim, Hyeok Ju Park, Hee Young Kim, Seung Eun Lee, Wonwoong Lee, Yoon Ha, Sungho Park, Jieun Kim, Won‐Young Cho, Woong Sun, Jong‐Seo Kim, Junsang Yoo   +18 more
wiley   +1 more source

Optimized Subgoal Generation in Hierarchical Reinforcement Learning for Coverage Path Planning

Automation
Hierarchical Reinforcement Learning (HRL) for UAV Coverage Path Planning (CPP) is hindered by the “subgoal space explosion”, causing inefficient exploration. To address this, we propose a two-stage framework, Hierarchical Reinforcement Learning Guided by
Yijun Zhang, Zhiming Li, Ku Du
doaj   +1 more source

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control. [PDF]

Inf Sci (N Y), 2023
Du X, Chen H, Yang B, Long C, Zhao S.
europepmc   +1 more source

Gut Microbiota‐Derived Anandamide Mediates the Therapeutic Effects of Urolithin A on Alcohol‐Induced Cognitive and Social Dysfunction via CB1R‐DRD2‐RAP1 Signaling Axis

Advanced Science, EarlyView.
This study reveals that Urolithin A (UA) counteracts alcohol‐induced cognitive and social dysfunction (AICSD) via a gut microbiome‐dependent mechanism. UA‐enriched Bacteroids sartorii and Parabacteroids distasonis elevate anandamide (AEA), which activates the CB1R‐DRD2‐Rap1 signaling cascade to drive synaptic repair and reduce neuroinflammation ...
Hongbo Zhang, Zibin Li, Yao Xiao, Ji Bian, Caian He, Chao Liu, Lan Gong, Lin Han, Zhigang Liu, Min Wang   +9 more
wiley   +1 more source

Exploring the limits of hierarchical world models in reinforcement learning

Scientific Reports
Hierarchical model-based reinforcement learning (HMBRL) aims to combine the sample efficiency of model-based reinforcement learning with the abstraction capability of hierarchical reinforcement learning.
Robin Schiewer, Anand Subramoney, Laurenz Wiskott   +2 more
doaj   +1 more source

Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT

IEEE Access
The Industrial Internet of Things (IIoT) presents significant challenges in task offloading, resource allocation, and energy efficiency, necessitating intelligent, scalable, and adaptive solutions.
S. Benila, K. Devi
doaj   +1 more source