Results 81 to 90 of about 6,522,305 (296)

Disease diagnosis and control by reinforcement learning techniques: a systematic literature review

Discover Computing
Disease diagnosis and control is one of the widely recognized and pursued research challenges in the field of reinforcement learning (RL). Early diagnosis and prevention of critical diseases are the major problems, and addressing these can help patients ...
Aditya Dev Mishra, Ajay Kumar Shrivastava, Megha Bhushan   +2 more
doaj   +1 more source

CayleyPy RL: Pathfinding and reinforcement learning on Cayley graphs

Advances in Theoretical and Mathematical Physics
This paper is the second in a series of studies on developing efficient artificial intelligence-based approaches to pathfinding on extremely large graphs (e.g. $10^{70}$ nodes) with a focus on Cayley graphs and mathematical applications. The open-source CayleyPy project is a central component of our research.
A. Chervov, A. Soibelman, S. Lytkin, Igor Kiselev, S. Fironov, A. Lukyanenko, A. Dolgorukova, A. Ogurtsov, F. Petrov, S. Krymskii, M. Evseev, L. Grunvald, D. Gorodkov, G. Antiufeev, G. Verbii, V. Zamkovoy, L. Cheldieva, I. Koltsov, A. Sychev, M. Obozov, A. Eliseev, S. Nikolenko, N. Narynbaev, R. Turtayev, N. Rokotyan, S. Kovalev, A. Rozanov, V. Nelin, S. Ermilov, L. Shishina, D. Mamayeva, A. Korolkova, K. Khoruzhii, A. Romanov   +33 more
openaire   +2 more sources

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

Sauté RL: Almost Surely Safe Reinforcement Learning Using State Augmentation [PDF]

, 2022
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications. For example, plane landing and take-off should ideally occur with probability one.
Wang, Ziyan, Sootla, Aivar, Mguni, David, Jafferjee, Taher, Cowen-Rivers, Alexander I, Bou-Ammar, Haitham, Wang, Jun   +6 more
core   +1 more source

Comparative Study of Reinforcement Learning and Null-Space Projection-Based Control Framework for a High-DoF Manipulator for Automated Coating

Actuators
The coating process of a ship-hull interior requires automation owing to significant occupational hazards associated with its working environment. The interior of a ship hull is large and structurally complex, and hence requires a highly redundant ...
Yeonwoo Mo, Changhyun Cho, Jungmin Kim, Sejin Kim   +3 more
doaj   +1 more source

Autonomous Navigation of Pollen‐Inspired Magnetic Microrobots for Biomedical Applications

Advanced Robotics Research, EarlyView.
A sunflower pollen‐inspired magnetic microrobot enables controlled rolling navigation in vessel‐like environments. Its open geometry reduces hydrodynamic drag, while vision‐based closed‐loop control, shortest‐path planning, and reinforcement learning support target‐reaching and maze navigation with microrobot‐scale accuracy, highlighting a route toward
Ali Anil Demircali, Abdurrahim Yilmaz, Meysam Keshavarz, Jinshi Zhao, Ara Darzi, Eric Yeatman, Burak Temelkuran   +6 more
wiley   +1 more source

Nonreciprocal Swarmalators With Reconfigurable and Controllable Formations for Robot Collectives

Advanced Robotics Research, EarlyView.
Nonreciprocal swarmalator interactions are enabled through control barrier functions to transform self‐organizing robot collectives into reconfigurable, constraint‐aware systems. Complex two‐ and three‐dimensional shapes, continuous morphing, obstacle‐aware navigation, collective splitting, and object transport emerge from modulating agent‐level ...
Kush Patel, Xinyue Xu, Wei Xiao, Steven Ceron   +3 more
wiley   +1 more source

Towards Automated Reinforcement Learning

, 2022
LAUREA MAGISTRALEL’Automated Reinforcement Learning (AutoRL) é un’area di ricerca relativamente nuova, che recentemente sta ricevendo sempre piú attenzione, il cui obbiettivo é quello di facilitare l’utilizzo di tecniche di Reinforcement Learning (RL)
Lombarda, Davide
core  

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL

CoRR
The rapid advancement of machine learning (ML) has led to its increasing integration into cyber-physical systems (CPS) across diverse domains. While CPS offer powerful capabilities, incorporating ML components introduces significant safety and assurance challenges.
Calum C. Imrie, Ioannis Stefanakos, Sepeedeh Shahbeigi, Richard Hawkins 0001, Simon Burton 0001   +4 more
openaire   +3 more sources