Results 71 to 80 of about 544,487 (316)
Adaptive Control with Approximated Policy Search Approach
ITB Journal of Engineering Science, 2010 Most of existing adaptive control schemes are designed to minimize error between plant state and goal state despite the fact that executing actions that are predicted to result in smaller errors only can mislead to non-goal states. We develop an adaptive
Agus Naba
doaj +1 more source
Discrete Event Modeling and Simulation for Reinforcement Learning System Design
Information, 2022 Discrete event modeling and simulation and reinforcement learning are two frameworks suited for cyberphysical system design, which, when combined, can give powerful tools for system optimization or decision making process for example.
Laurent Capocchi +1 more
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Stress affects instrumental learning based on positive or negative reinforcement in interaction with personality in domestic horses. [PDF]
PLoS ONE, 2017 The present study investigated how stress affects instrumental learning performance in horses (Equus caballus) depending on the type of reinforcement. Horses were assigned to four groups (N = 15 per group); each group received training with negative or ...
Mathilde Valenchon +3 more
doaj +1 more source
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Advanced Engineering Materials, EarlyView.Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani +2 more
wiley +1 more source
Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
AlgorithmsIn recent years, with the rapid advancements in Natural Language Processing (NLP) technologies, large models have become widespread. Traditional reinforcement learning algorithms have also started experimenting with language models to optimize training ...
Jiaming Li, Ning Xie, Tingting Zhao
doaj +1 more source
Advanced Engineering Materials, EarlyView.This manuscript presents advances in digital transformation within materials science and engineering, emphasizing the role of the MaterialDigital Initiative. By testing and applying concepts such as ontologies, knowledge graphs, and integrated workflows, it promotes semantic interoperability and data‐driven innovation. The article reviews collaborative
Bernd Bayerlein +44 more
wiley +1 more source
Robust Reinforcement Learning with Distributional Risk-averse formulation [PDF]
arXiv, 2022 Robust Reinforcement Learning tries to make predictions more robust to changes in the dynamics or rewards of the system. This problem is particularly important when the dynamics and rewards of the environment are estimated from the data. In this paper, we approximate the Robust Reinforcement Learning constrained with a $\Phi$-divergence using an ...
arxiv
Advanced Engineering Materials, EarlyView.The MaterialDigital initiative drives the digital transformation of material science by promoting findable, accessible, interoperable, and reusable principles and enhancing data interoperability. This article explores the role of scientific workflows, highlights challenges in their adoption, and introduces the Workflow Store as a key tool for sharing ...
Simon Bekemeier +37 more
wiley +1 more source
, 2022 Electricity prices have risen significantly year on year and reducing energy use in homes can save money, improve energy security and reduce pollution from non-renewable energy sources. Whether to lower the monthly electricity bills or be concerned about the home's carbon footprint, reducing energy is helpful.
Robert H. Chen, Chelsea Chen
openaire +3 more sources
Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents [PDF]
arXiv, 2021 Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Strong AI. Reinforcement learning is similar to learning in biology. It is of great significance to study the combination of SNN and RL.
arxiv