Results 91 to 100 of about 34,803,504 (287)
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
Evaluation of reinforcement learning in transformer-based molecular design
Journal of CheminformaticsDesigning compounds with a range of desirable properties is a fundamental challenge in drug discovery. In pre-clinical early drug discovery, novel compounds are often designed based on an already existing promising starting compound through structural ...
Jiazhen He +6 more
doaj +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
Frontiers in Neuroscience, 2014 Computational models of learning have proved largely successful in characterising potentialmechanisms which allow humans to make decisions in uncertain and volatile contexts.
Elaine eDuffin +3 more
doaj +1 more source
Model-Based Transfer Learning for Contextual Reinforcement Learning
Advances in Neural Information Processing Systems 3738th Conference on Neural Information Processing Systems (NeurIPS 2024)
Jung-Hoon Cho +3 more
openaire +3 more sources
A Contraction Approach to Model-based Reinforcement Learning
CoRR, 2020 Despite its experimental success, Model-based Reinforcement Learning still lacks a complete theoretical understanding. To this end, we analyze the error in the cumulative reward using a contraction approach. We consider both stochastic and deterministic state transitions for continuous (non-discrete) state and action spaces.
Ting-Han Fan, Peter J. Ramadge
openaire +4 more sources
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
A strategy learning model for autonomous agents based on classification
International Journal of Applied Mathematics and Computer Science, 2015 In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered
Śnieżyński Bartłomiej
doaj +1 more source
Model-Based Reinforcement Learning Under Confounding
CoRRWe investigate model-based reinforcement learning in contextual Markov decision processes (C-MDPs) in which the context is unobserved and induces confounding in the offline dataset. In such settings, conventional model-learning methods are fundamentally inconsistent, as the transition and reward mechanisms generated under a behavioral policy do not ...
Nishanth Venkatesh +1 more
openaire +3 more sources
What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source