Results 81 to 90 of about 10,350,910 (281)

WRFMR: A Multi-Agent Reinforcement Learning Method for Cooperative Tasks

IEEE Access, 2020
Multi-agent reinforcement learning (MARL) for cooperative tasks has been extensively studied in recent years. The balance of exploration and exploitation is crucial to MARL algorithms' performance in terms of the learning speed and the quality of the ...
Hui Liu, Zhen Zhang, Dongqing Wang
doaj   +1 more source

LLM Collaboration with Multi-Agent Reinforcement Learning

Proceedings of the AAAI Conference on Artificial Intelligence
A large amount of work has been done in Multi-Agent Systems (MAS) for modeling and solving problems with multiple interacting agents. However, most LLMs are pretrained independently and not specifically optimized for coordination. Existing LLM fine-tuning frameworks rely on individual rewards, which require complex reward designs for each agent to ...
Liu, Shuo, Chen, Tianle, Liang, Zeyu, Lyu, Xueguang, Amato, Christopher   +4 more
openaire   +3 more sources

Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics

Advanced Materials, EarlyView.
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair, Hrisheekesh Thachoth Chandran, Karl Leo, Hans Kleemann   +3 more
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Curriculum Learning Framework Based on Reinforcement Learning in Sparse HeterogeneousMulti-agent Environments [PDF]

Jisuanji kexue
The battlefield of modern warfare is large and has a variety of units,and the use of multi-agent reinforcement learning(MARL) in battlefield simulation can enhance the collaborative decision-making ability among combat units and thus improve combat ...
LUO Ruiqing, ZENG Kun, ZHANG Xinjing
doaj   +1 more source

Sustainable and Multifunctional Natural Macromolecular Polymers for Aqueous Zn Metal Batteries

Advanced Materials, Volume 38, Issue 57, 13 October 2026.
We comprehensively review the structure‐function relationships and regulatory mechanism of natural macromolecular polymers in stabilizing zinc anodes at the microscopic and mesoscopic scales. The interactions among polymer structures, optimization strategies, and regulatory mechanisms are discussed systematically summarizing recent related research ...
Yunuo Shi, Bo Jiang, Jingcheng Zhang, Xiaodong Shao, Peixun Xiong, Lutong Shan, Shengyang Huang, Mengfang Liang, Mengyao Chang, Fangyan Liu, Wenwu Li, Baoyu Xia, Wei Ji, Ho Seok Park   +13 more
wiley   +1 more source

Transfer Learning in Multi-Agent Reinforcement Learning Domains [PDF]

, 2012
In the context of reinforcement learning, transfer learning refers to the concept of reusing knowledge acquired in past tasks to speed up the learning procedure in new tasks. Transfer learning methods have been succesfully applied in single-agent reinforcement learning algorithms, but no prior work has focused on applying them in a multi-agent ...
Georgios Boutsioukis, Ioannis Partalas, Ioannis P. Vlahavas   +2 more
openaire   +1 more source

Electrolyte Design for Fast‐Charging Lithium‐Based Batteries

Advanced Materials, Volume 38, Issue 57, 13 October 2026.
A decade of progress in fast‐charging electrolytes for lithium batteries is reviewed. Electrolyte design strategies spanning solvents, salts, additives, and advanced systems, such as localized high‐concentration electrolytes (LHCEs), are summarized. Advanced diagnostic tools for lithium plating and interphase chemistry are discussed, with perspectives ...
Chen Liu, Zehao Cui, Arumugam Manthiram
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