Multi-agent reinforcement learning

Results 221 to 230 of about 139,994 (273)

Multi-Agent Reinforcement Learning-Based Computation Offloading for Unmanned Aerial Vehicle Post-Disaster Rescue. [PDF]

Sensors (Basel)
Wang L, Jiao H.
europepmc +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source

Decentralized multi-agent reinforcement learning based on best-response policies. [PDF]

Front Robot AI
Gabler V, Wollherr D.
europepmc +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang +3 more
wiley +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa +2 more
wiley +1 more source

A Cascaded Multi-Agent Reinforcement Learning-Based Resource Allocation for Cellular-V2X Vehicular Platooning Networks. [PDF]

Sensors (Basel)
Narayanasamy I, Rajamanickam V.
europepmc +1 more source

Enhancing Sensitivity across Scales with Highly Sensitive Hall Effect‐Based Auxetic Tactile Sensors

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Herein, a tactile sensor based on hall‐effect sensors with an auxetic structure, called Hall effect‐based auxetic tactile sensor (HEATS), is proposed. The change in magnetism resulting from the deformation of the auxetic structure is utilized for sensing.
Youngheon Yun +6 more
wiley +1 more source

Self‐Sensing Artificial‐Muscle‐Empowered Humanlike Perception, Interaction, and Positioning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The proposed self‐sensorized artificial muscle (SSAM) can sense its length change as small as 0.01 mm via a seamlessly integrated multi‐segment induction coil. The SSAM provides accurate length information regardless of its loadings, driving pressure, or muscle design, adequate for robust data‐driven feedback control.
Houping Wu +6 more
wiley +1 more source

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam +2 more
wiley +1 more source

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study. [PDF]

PLoS One
Lussange J, Vrizzi S, Palminteri S, Gutkin B. +3 more
europepmc +1 more source

reinforcement learning
deep reinforcement learning
multi-agent system

multi-agent systems