Deep reinforcement learning approach to optimize the driving performance of shield tunnelling machines

K Elbaz, A Zhou, SL Shen - Tunnelling and Underground Space …, 2023 - Elsevier
This paper proposes a deep reinforcement learning (DRL)-based model as a valuable tool
to improve the performance of the driving system (ie thrust force and cutterhead torque) of a …

强化学习研究综述

陈学松, 杨宜民 - 计算机应用研究, 2010 - cqvip.com
在未知环境中, 关于agent 的学习行为是一个既充满挑战又有趣的问题, 强化学习通过试探与
环境交互获得策略的改进, 其学习和在线学习的特点使其成为机器学习研究的一个重要分支 …

Real-time optimization using reinforcement learning

KM Powell, D Machalek, T Quah - Computers & Chemical Engineering, 2020 - Elsevier
This work introduces a novel methodology for real-time optimization (RTO) of process
systems using reinforcement learning (RL), where optimal decisions in response to external …

Reinforcement learning using quantum Boltzmann machines

D Crawford, A Levit, N Ghadermarzy, JS Oberoi… - arXiv preprint arXiv …, 2016 - arxiv.org
We investigate whether quantum annealers with select chip layouts can outperform classical
computers in reinforcement learning tasks. We associate a transverse field Ising spin …

A deep reinforcement learning-based autonomous ventilation control system for smart indoor air quality management in a subway station

SK Heo, KJ Nam, J Loy-Benitez, Q Li, SC Lee… - Energy and …, 2019 - Elsevier
Mechanical ventilation has been widely implemented to alleviate poor indoor air quality
(IAQ) in confined underground public facilities. However, due to time-varying IAQ properties …

A survey on applications of agent technology in industrial process control

M Metzger, G Polakow - IEEE Transactions on Industrial …, 2011 - ieeexplore.ieee.org
The agents and multiagent systems technology is actively researched by the academia and
industrial community. However, the technology is particularly popular in the manufacturing …

[HTML][HTML] Scalable reinforcement learning for plant-wide control of vinyl acetate monomer process

L Zhu, Y Cui, G Takami, H Kanokogi… - Control Engineering …, 2020 - Elsevier
This paper explores a reinforcement learning (RL) approach that designs automatic control
strategies in a large-scale chemical process control scenario as the first step for leveraging …

Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in Plectonema by artificial neural network and deep reinforcement …

Y Ma, DA Noreña-Caro, AJ Adams, TB Brentzel… - Computers & Chemical …, 2020 - Elsevier
In this paper, a model-free deep reinforcement learning (DRL) strategy is presented with an
artificial neural network (ANN) as reaction simulation environment, to obtain a fed-batch …

Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning

H Li, CR Collins, TG Ribelli, K Matyjaszewski… - … Systems Design & …, 2018 - pubs.rsc.org
We devise a novel technique to control the shape of polymer molecular weight distributions
(MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent …

Alleviating parameter-tuning burden in reinforcement learning for large-scale process control

L Zhu, G Takami, M Kawahara, H Kanokogi… - Computers & Chemical …, 2022 - Elsevier
Modern process controllers necessitate high quality models and remedial system re-
identification upon performance degradation. Reinforcement Learning (RL) can be a …