Results 101 to 110 of about 8,331,707 (294)

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

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, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

You can now have a chance to beat your grandparents at Briscola thanks to Deep Reinforcement Learning

, 2023
openDevelopment and training of an artificial intelligent agent through Reinforcement Learning, Deep Learning and Game Theory, able to play the game of Briscola.Development and training of an artificial intelligent agent through Reinforcement Learning ...
SINIGAGLIA, ALBERTO
core  

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, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning

Sensors
Reinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment.
Yana Yang, Meng Xi, Huiao Dai, Jiabao Wen, Jiachen Yang   +4 more
doaj   +1 more source

Deep Reinforcement Learning with Double Q-Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively.
Hado van Hasselt, Arthur Guez, David Silver 0001   +2 more
openaire   +4 more sources

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Dynamic optimization of stand structure in Pinus yunnanensis secondary forests based on deep reinforcement learning and structural prediction

Frontiers in Plant Science
IntroductionThe rational structure of forest stands plays a crucial role in maintaining ecosystem functions, enhancing community stability, and ensuring sustainable management.
Jian Zhao, Jianming Wang, Jiting Yin, Yuling Chen, Baoguo Wu   +4 more
doaj   +1 more source

Learning to Walk Via Deep Reinforcement Learning

Robotics: Science and Systems XV, 2019
RSS 2019, https://sites.google.com/view/minitaur-locomotion/
Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan 0001, George Tucker, Sergey Levine   +5 more
openaire   +3 more sources

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Deep Reinforcement Learning algorithms learn important classes of repeated games optimally—Theoretical and empirical analysis

Franklin Open
This paper evaluates two prominent Deep Reinforcement Learning algorithms, Deep Q-Learning and Twin Delayed Deep Deterministic Policy Gradient, by comparing their learned policies against analytically derived optimal policies in specific game-theoretic ...
Marvin Bongiovi
doaj   +1 more source