Results 41 to 50 of about 10,350,910 (281)
IET Smart Grid, 2022 Residential buildings are large consumers of energy. They contribute significantly to the demand placed on the grid, particularly during hours of peak demand.
Junlin Lu, Patrick Mannion, Karl Mason
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Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade [PDF]
, 2012 In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when ...
Burt, Graeme +3 more
core +4 more sources
SC-MAIRL: Semi-Centralized Multi-Agent Imitation Reinforcement Learning
IEEE Access, 2023 Multi-agent reinforcement learning (MARL) is a challenging branch of reinforcement learning that requires cooperation of interactive learning agents to achieve individual objectives as well as shared team objectives.
Paul Brackett, Siming Liu, Yan Liu
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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
Reducing Q-Value Estimation Bias via Mutual Estimation and Softmax Operation in MADRL
AlgorithmsWith the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies.
Zheng Li +4 more
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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
Low-Cost Multi-Agent Navigation via Reinforcement Learning With Multi-Fidelity Simulator
IEEE Access, 2021 In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks.
Jiantao Qiu +6 more
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Learning to Communicate with Deep Multi-Agent Reinforcement Learning
CoRR, 2016 We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks.
Foerster, J +3 more
openaire +5 more sources
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 +4 more
wiley +1 more source
Design of Multi-Agent Angle Tracking Method Based on Deep Reinforcement Learning [PDF]
Jisuanji gongchengIn intelligent situational awareness application scenarios, multi-agent angle tracking problems often occur when moving targets must be monitored and controlled.
BI Qian, QIAN Cheng, ZHANG Ke, WANG Cheng
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