Results 101 to 110 of about 34,803,504 (287)
Discover Artificial IntelligenceBased on graph neural networks and reinforcement learning, this study designs three algorithms for multi-objective tourism route planning under different constraints: unconstrained, capacity-constrained, and demand-splitting scenarios.
YinChao Ma
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Research on predicting 2D-HP protein folding using reinforcement learning with full state space
BMC Bioinformatics, 2019 Background Protein structure prediction has always been an important issue in bioinformatics. Prediction of the two-dimensional structure of proteins based on the hydrophobic polarity model is a typical non-deterministic polynomial hard problem ...
Hongjie Wu +5 more
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Advanced Engineering Materials, EarlyView.Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios +5 more
wiley +1 more source
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
, 2017 In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks. We focus on evaluating transition function estimation, while we defer planning over this model to future work.
Jonker, C.M. (author) +2 more
core
Decentralized Bayesian reinforcement learning for online agent collaboration [PDF]
, 2012 Solving complex but structured problems in a decentralized manner via multiagent collaboration has received much attention in recent years. This is natural, as on one hand, multiagent systems usually possess a structure that determines the allowable ...
Farinelli, A. +6 more
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Mesolimbic confidence signals guide perceptual learning in the absence of external feedback
eLife, 2016 It is well established that learning can occur without external feedback, yet normative reinforcement learning theories have difficulties explaining such instances of learning.
Matthias Guggenmos +3 more
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Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Asynchronous Methods for Model-Based Reinforcement Learning
CoRR, 2019 10 pages, CoRL ...
Yunzhi Zhang +3 more
openaire +3 more sources
Advanced Engineering Materials, EarlyView.Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source