Results 51 to 60 of about 6,930,474 (290)
Constrained Reinforcement Learning in Hard Exploration Problems
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 One approach to guaranteeing safety in Reinforcement Learning is through cost constraints that are dependent on the policy. Recent works in constrained RL have developed methods that ensure constraints are enforced even at learning time while maximizing the overall value of the policy.
Pathmanathan Pankayaraj +1 more
openaire +3 more sources
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
Constrained Reinforcement Learning for Dynamic Material Handling
2023 International Joint Conference on Neural Networks (IJCNN), 2023 accepted by the 2023 International Joint Conference on Neural Networks (IJCNN)
Chengpeng Hu +5 more
openaire +3 more sources
SensorsResource-constrained intelligent systems increasingly require reliable long-form reasoning capabilities under limited computational and memory budgets, particularly in edge and embedded sensing environments.
Ziqin Yan +3 more
doaj +1 more source
Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Reinforcement-Learning-Based IDS for 6LoWPAN
, 2021 The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs).
Aryan Mohammadi Pasikhani (11456185) +2 more
core +1 more source
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
Continual Learning as Computationally Constrained Reinforcement Learning
Foundations and Trends® in Machine LearningAn agent that accumulates knowledge to develop increasingly sophisticated skills over a long lifetime could advance the frontier of artificial intelligence capabilities. The design of such agents, which remains a long-standing challenge, is addressed by the subject of continual learning.
Saurabh Kumar 0004 +6 more
openaire +4 more sources
Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning [PDF]
, 2005 PhDThis thesis investigates reinforcement learning algorithms suitable for learning in large state space problems and coevolution. In order to learn in large state spaces, the state space must be collapsed to a computationally feasible size and then ...
Tokarchuk, Laurissa Nadia
core +4 more sources
Fairness-Constrained Dynamic Pricing via Shielded Deep Reinforcement Learning
MathematicsFirms increasingly develop dynamic pricing policies to maximize revenue for perishable products with limited inventory over a finite selling horizon. This trend is enabled by the growing availability of sales data and is observed across industries such ...
Wenchuan Qiao +4 more
doaj +1 more source