Results 71 to 80 of about 137,924 (252)

A Theoretical Analysis of Cooperative Behavior in Multi-Agent Q-learning [PDF]

A number of experimental studies have investigated whether cooperative behavior may emerge in multi-agent Q-learning. In some studies cooperative behavior did emerge, in others it did not. This report provides a theoretical analysis of this issue.
Kaymak, U., Waltman, L.R.
core   +1 more source

Kindness in Multi-Agent Reinforcement Learning

, 2023
In human societies, people often incorporate fairness in their decisions and treat reciprocally by being kind to those who act kindly. They evaluate the kindness of others' actions not only by monitoring the outcomes but also by considering the intentions.
Alamiyan-Harandi, Farinaz, Hassanjani, Mersad, Ramazi, Pouria   +2 more
openaire   +2 more sources

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

A New Multi-Agent Reinforcement Learning Method Based on Evolving Dynamic Correlation Matrix

IEEE Access, 2019
Multi-agent reinforcement learning approaches can be roughly classified into two categories. One is the agent-based approach which can be implemented in real distributed systems, though most approaches of this type cannot provide meaningful theoretical ...
Xingli Gan, Hongliang Guo, Zhan Li
doaj   +1 more source

Reward-Reinforced Reinforcement Learning for Multi-agent Systems

, 2021
9 pages, 9 ...
Zheng, Changgang, Yang, Shufan, Parra-Ullauri, Juan, Garcia-Dominguez, Antonio, Bencomo, Nelly   +4 more
openaire   +2 more sources

Sculpting the Future of Bone: The Evolution of Absorbable Materials in Orthopedics

Advanced Materials, EarlyView.
This review summarizes the current status of polymeric, ceramic, and metallic absorbable materials in orthopedic applications, and highlights several innovative strategies designed to enhance mechanical performance, control degradation, and promote bioactivity. We also discuss the progress and translational potential of absorbable materials in treating
Zhao Wang, Zehui Lv, Xuejie Cai, Yingjie Wang, Bo Peng, Haojing Xu, Hualuo Pang, Xingdong Yang, Jiawei Xu, Yixin Bian, Bin Feng, Peng Wen, Yufeng Zheng, Xisheng Weng   +13 more
wiley   +1 more source

Multi-UAV pursuit-evasion gaming based on PSO-M3DDPG schemes

Complex & Intelligent Systems
The sample data for reinforcement learning algorithms often exhibit sparsity and instability, making the training results susceptible to falling into local optima.
Yaozhong Zhang, Meiyan Ding, Jiandong Zhang, Qiming Yang, Guoqing Shi, Meiqu Lu, Frank Jiang   +6 more
doaj   +1 more source

Role Learning-based Multi-Agent Reinforcement Learning Methods [PDF]

Jisuanji gongcheng
Multi-Agent Reinforcement Learning (MARL) plays a crucial role in solving complex cooperative tasks. However, traditional methods face significant limitations in dynamic environments and information nonstationarity.
SHEN Sitong, WANG Yaowu, XIE Zaipeng, TANG Bin
doaj   +1 more source

Review of Thin Lithium Metal Battery Anode Fabrication – Microstructure – Electrochemistry Relations

Advanced Materials, EarlyView.
Thin, lightweight lithium‐metal anodes are pivotal for practical high‐energy batteries. This review surveys processing routes that convert diverse Li precursors, e.g., ingots, melts, solutions, and vapor, into Li‐rich foils with controlled thickness, areal density, and tailored functionality.
Yuhang Hu, Zidong Chen, Yixian Wang, Haorui Hou, Bingcheng Chen, David Mitlin, Wei Liu   +6 more
wiley   +1 more source

Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement Learning

Multimodal Technologies and Interaction
Today, reinforcement learning is one of the most effective machine learning approaches in the tasks of automatically adapting computer systems to user needs. However, implementing this technology into a digital product requires addressing a key challenge:
Dmitry Vidmanov, Alexander Alfimtsev
doaj   +1 more source