Results 81 to 90 of about 6,531,013 (293)
Safe and Efficient Operation with Constrained Hierarchical Reinforcement Learning
, 2023 Hierarchical Reinforcement Learning (HRL) holds the promise of enhancing sample efficiency and generalization capabilities of Reinforcement Learning (RL) agents by leveraging task decomposition and temporal abstraction, which aligns with human reasoning.
Günnemann, Stephan +2 more
core +1 more source
Quantum Reinforcement Learning [PDF]
, 2005 A novel quantum reinforcement learning is proposed through combining quantum theory and reinforcement learning. Inspired by state superposition principle, a framework of state value update algorithm is introduced. The state/action value is represented with quantum state and the probability of action eigenvalue is denoted by probability amplitude, which
Daoyi Dong +2 more
openaire +2 more sources
Applied Sciences, 2018 To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed.
Linfei Yin +3 more
doaj +1 more source
Arthritis Care &Research, EarlyView.Objective To evaluate how modifiable psychosocial factors and fatigue relate to physical functioning in patients with systemic lupus erythematosus (SLE). Methods In this cross‐sectional study of two demographically distinct cohorts (Approaches to Positive, Patient‐Centered Experiences of Aging with Lupus [APPEAL] and California Lupus Epidemiology Study
Mrinalini Dey +8 more
wiley +1 more source
Learning Strict Nash Equilibria through Reinforcement [PDF]
This paper studies the analytical properties of the reinforcement learning model proposed in Erev and Roth (1998), also termed cumulative reinforcement learning in Laslier et al (2001).
Ianni, Antonella
core
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
Probability Matching and Reinforcement Learning* [PDF]
Probability matching occurs when an action is chosen with a frequency equivalent to the probability of that action being the best choice. This sub-optimal behavior has been reported repeatedly by psychologist and experimental economist.
Javier Rivas
core
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
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Refinement of biologically inspired models of reinforcement learning [PDF]
, 2010 Reinforcement learning occurs when organisms adapt the propensities of given behaviours on the basis of associations with reward and punishment. Currently, reinforcement learning models have been validated in minimalist environments in which only 1-2 ...
Aquili, Luca
core +2 more sources