Results 71 to 80 of about 10,350,910 (281)

ATA-MAOPT: Multi-Agent Online Policy Transfer Using Attention Mechanism With Time Abstraction

IEEE Access
Multi-agent deep reinforcement learning inherits one of the shortcomings of reinforcement learning, which is that a very large number of experimental episodes need to be performed to train an agent model to learn the optimal policy.
Chao Wang, Xiangbin Zhu
doaj   +1 more source

Microfluidic Engineering of Microgels: A Review of Emerging Technologies, Materials, Fabrication Strategies, and Applications

Advanced Healthcare Materials, EarlyView.
Recent advances in microfluidic technologies for microgel fabrication are critically reviewed, highlighting the capabilities and limitations of major microfluidic platforms and discussing how platform design, material selection, and crosslinking strategies influence microgel architecture and functionality. Current challenges and future opportunities in
Afshin Abrishamkar, Laura Neely, Cynthia Pham, Nada Ibrahim, Alexis Larios‐Cervantes, Ben A. Kertesz, Todd Hoare   +6 more
wiley   +1 more source

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

Using reinforcement learning to coordinate better

, 2005
This paper examines the potential and the impact of introducing learning capabilities into autonomous agents that make decisions at run-time about which mechanism to exploit in order to coordinate their activities. Specifically, our motivating hypothesis
Jennings, N. R., Excelente-Toledo, CB, Nicholas R. Jennings, Excelente-Toledo, C.B., Jennings, NR, Cora B. Excelente-Toledo   +5 more
core   +2 more sources

Multi agent physics informed reinforcement learning for waterflooding optimization

Franklin Open
Waterflooding optimization is a critical process for enhancing oil recovery in mature oil fields, where conventional approaches often rely on fixed injection rates over an extended period.
Ramez Abdalla, Nermine Agban, Christian Lüddeke, Dan Sui, Philip Jaeger   +4 more
doaj   +1 more source

Learning when and how to co-ordinate [PDF]

, 2003
This paper examines the potential and the impact of introducing learning capabilities into autonomous agents that make decisions at run-time about which mechanism to exploit in order to coordinate their activities.
Jennings, N. R., Excelente-Toledo, CB, Excelente-Toledo, C.B., Jennings, NR   +3 more
core  

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +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

Multi-agent reinforcement learning for planning and scheduling multiple goals

, 2000
Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition of the multiagent systems. However, most researches on multiagent system applying a reinforcement learning algorithm focus on the method to reduce ...
Arai, Sachiyo, Sycara, Katia, Payne, Terry R.   +2 more
core   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source