Results 81 to 90 of about 6,944,236 (307)
Reinforcement learning–optimized checkpoints for DrugFlow-RL, a framework for pocket-conditioned molecular generation with improved synthesizability. These checkpoints were obtained by applying online and offline reinforcement learning to DrugFlow, a ...
Bruno E. Correia (14820990) +5 more
core +1 more source
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 +3 more
wiley +1 more source
Refined Risk Management in Safe Reinforcement Learning with a Distributional Safety Critic
, 2022 Safety is critical to broadening the real-world use of reinforcement learning (RL). Modeling the safety aspects using a safety-cost signal separate from the reward is becoming standard practice, since it avoids the problem of finding a good balance ...
Yang, Q. (author) +3 more
core
Continuum Robots in Surgery: From Engineering Design to Clinical Practice
Advanced Robotics Research, EarlyView.The hyper‐redundant nature of continuum robot structures enables near‐infinite degrees of freedom leading to immense potential for medical applications. This review provides a historical background of continuum robots and novel ideas shaping their progress to current structural design, sensing technology, modeling techniques, and control methods.
Griffin Smith +6 more
wiley +1 more source
Disease diagnosis and control by reinforcement learning techniques: a systematic literature review
Discover ComputingDisease 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 +2 more
doaj +1 more source
Intelligent Inventory Control via Ruminative Reinforcement Learning
Journal of Applied Mathematics, 2014 Inventory management is a sequential decision problem that can be solved with reinforcement learning (RL). Although RL in its conventional form does not require domain knowledge, exploiting such knowledge of problem structure, usually available in ...
Tatpong Katanyukul, Edwin K. P. Chong
doaj +1 more source
CayleyPy RL: Pathfinding and reinforcement learning on Cayley graphs
Advances in Theoretical and Mathematical PhysicsThis 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 +33 more
openaire +2 more sources
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 +6 more
wiley +1 more source
Advances in Reinforcement Learning
, 2011 Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of
core +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 +3 more
wiley +1 more source