Results 61 to 70 of about 6,522,305 (296)

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

Artificial Intelligence Meets Micro/Nanorobotics

Advanced Materials, EarlyView.
Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever, Minitha Cherukutty Ramakrishnan, Ian Kuula Ross, Ivan Zelinka, Hamed Shahsavan, Michal Otyepka, Martin Pumera   +6 more
wiley   +1 more source

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Advanced Robotics Research, EarlyView.
A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo, Jiahui Zhang, Yimin Han, Ji Ma, Minghao Lu, Ioannis Havoutis, Peng Lu   +6 more
wiley   +1 more source

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

Advanced Robotics Research, EarlyView.
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source

Dynamic Multichannel Sensing in Cognitive Radio: Hierarchical Reinforcement Learning

IEEE Access, 2021
Efficient use of spectral resources is critical in wireless networks and has been extensively studied in recent years. Dynamic spectrum access (DSA) is one of the key techniques on utilizing the spectral resources. Among them, reinforcement learning (RL)
Shuai Liu, Jiayun Wu, Jing He
doaj   +1 more source

Nonlocomotory Robotic Strategies for Dynamic Rotation Control in Terrestrial Robots: A Review

Advanced Robotics Research, EarlyView.
Terrestrial robots increasingly require rapid body rotation to maintain stability and agility in complex environments. This review shows nonlocomotory rotational control strategies that operate without ground contact, including reaction wheels, tails, bars, limbs, and thrusters.
Y. Liang, J. An, X. Zheng, D. Wang, S. Wang, X. Wang, P. Zhu, J. Pei, S. Li, J. Paripurana, Y. Han, Z. Tang, X. Gan, Q. He, X. Ma   +14 more
wiley   +1 more source

Evaluating reinforcement learning for game theory application learning to price airline seats under competition

, 2009
Applied Game Theory has been criticised for not being able to model real decision making situations. A game's sensitive nature and the difficultly in determining the utility payoff functions make it hard for a decision maker to rely upon any game ...
Collins, Andrew
core   +1 more source

RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker [PDF]

IEEE/ACM Transactions on Networking, 2019
Network slicing is considered one of the mainpillars of the upcoming 5G networks. Indeed, the ability toslice a mobile network and tailor each slice to the needs ofthe corresponding tenant is envisioned as a key enabler forthe design of future networks. However, this novel paradigmopens up to new challenges, such as isolation between networkslices, the
Vincenzo Sciancalepore, Xavier Costa-Pérez, Albert Banchs   +2 more
openaire   +5 more sources

Model-Based Reinforcement Learning with Continuous States and Actions [PDF]

, 2008
22.10.13 KB. Ok to add the published version to spiral. ESANNFinding an optimal policy in a reinforcement learning (RL) framework with continuous state and action spaces is challenging. Approximate solutions are often inevitable.
Rasmussen, Carl E, Deisenroth, Marc P, Rasmussen, C. E., Peters, Jan, Deisenroth, M., Deisenroth, M. P., Rasmussen, C., Peters, J.   +7 more
core   +1 more source

Artificial intelligence and reinforcement learning

, 2022
In my thesis I would like to discuss AI (Artificial Intelligence), its connection to RL (Reinforcement Learning), and how to incorporate these ideas into a game.
Khedr, Mohamed
core