Search Results - "Deep deterministic policy gradient algorithm"

Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach by Liu, Yongkui, Liang, Huagang, Xiao, Yingying, Zhang, Haifeng, Zhang, Jingxin, Zhang, Lin, Wang, Lihui

“…•A Deep Deterministic Policy Gradient (DDPG)-based approach to service composition in cloud manufacturing is proposed.•Performance of DDPG is examined in both…”
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Multi-Agent Deep Reinforcement Learning for Urban Traffic Light Control in Vehicular Networks by Wu, Tong, Zhou, Pan, Liu, Kai, Yuan, Yali, Wang, Xiumin, Huang, Huawei, Wu, Dapeng Oliver

“…As urban traffic condition is diverse and complicated, applying reinforcement learning to reduce traffic congestion becomes one of the hot and promising…”
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Adaptive neuro-fuzzy PID controller based on twin delayed deep deterministic policy gradient algorithm by Shi, Qian, Lam, Hak-Keung, Xuan, Chengbin, Chen, Ming

“…This paper presents an adaptive neuro-fuzzy PID controller based on twin delayed deep deterministic policy gradient (TD3) algorithm for nonlinear systems. In…”
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Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency by Touzani, Samir, Prakash, Anand Krishnan, Wang, Zhe, Agarwal, Shreya, Pritoni, Marco, Kiran, Mariam, Brown, Richard, Granderson, Jessica

“…Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly…”
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Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning by Li, Jiawen, Yu, Tao, Zhang, Xiaoshun

“…•A novel coordinated automatic generation control algorithm framework is proposed.•A deep reinforcement learning algorithm is introduced to obtains better…”
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Energy management strategy for hybrid electric vehicles based on deep reinforcement learning with consideration of electric drive system thermal characteristics by Qin, Juhuan, Huang, Haozhong, Lu, Hualin, Li, Zhaojun

“…•An optimized energy management strategy based on deep deterministic policy gradient is proposed.•The strategy considers the thermal characteristics of the…”
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Data-driven voltage/var optimization control for active distribution network considering PV inverter reliability by Zhang, Bo, Gao, Yuan

“…•The XGBoost junction temperature calculation model is used to improve the junction temperature calculation efficiency and reduce the influence of IGBT…”
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Multiagent-based deep reinforcement learning framework for multi-asset adaptive trading and portfolio management by Cheng, Li-Chen, Sun, Jian-Shiou

“…The highly dynamic nature of stock markets has motivated researchers to propose various supervised learning models to assist investors to optimize financial…”
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Optimal dual-model controller of solid oxide fuel cell output voltage using imitation distributed deep reinforcement learning by Li, Jiawen, Cui, Haoyang, Jiang, Wei, Yu, Hengwen

“…Solid oxide fuel cell (SOFC) is disadvantaged by significant nonlinearity, which makes it difficult to control output voltage of SOFC and satisfy the…”
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Coordinated automatic generation control of interconnected power system with imitation guided exploration multi-agent deep reinforcement learning by Li, Jiawen, Yu, Tao, Zhang, Xiaoshun

“…•An intelligent automatic power generation control (IAGC) framework is proposed.•The proposed framework can improve the dynamic performance and reduce…”
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A novel data-driven model-free synchronization protocol for discrete-time multi-agent systems via TD3 based algorithm by Liu, Zhongxin, Li, Ye, Lan, Ge, Chen, Zengqiang

“…System modeling is a complex and time-consuming task in engineering, and traditional model-based or observer-based methods are unable to deal situations when…”
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Trajectory planning of redundant parallel mechanism considering motion accuracy based on reinforcement learning by Zeng, Chen-dong, Qiu, Zhi-cheng, Zhang, Fen-hua, Zhang, Xian-min

“…The motion accuracy of the trajectory directly affects the reliability of parallel mechanisms in precision tasks such as micro-assembly. Thus, this paper…”
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Autonomous intelligent control of earth pressure balance shield machine based on deep reinforcement learning by Liu, Xuanyu, Zhang, Wenshuai, Shao, Cheng, Wang, Yudong, Cong, Qiumei

“…In order to reduce the construction risk caused by human operation error and improve the geological adaptive ability of the shield machine, an autonomous…”
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Network-level aircraft trajectory planning via multi-agent deep reinforcement learning: Balancing climate considerations and operational manageability by Baneshi, Fateme, Cerezo-Magaña, María, Soler, Manuel

“…Optimizing flight trajectories emerges as a viable strategy to mitigate the non-CO2 climate impacts of aviation. However, integrating individually optimized…”
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UAV-Enabled Relaying and Edge Computing: Hybrid Network Optimization by Ma, Bo, Ye, Jiawei, Feng, Shaohan, Li, Chuanhuang, Niyato, Dusit, Kim, Dong In

“…This paper exploits an Unmanned Aerial Vehicle assisted Mobile Edge Computing (UAV-MEC) system to meet the computation demand of User Equipments (UEs)…”
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Tracking interval control for urban rail trains based on safe reinforcement learning by Lin, Junting, Qiu, Xiaohui, Li, Maolin

“…In order to solve the problem of controlling the interval between trains in the new train control system, which aims to ensure the safe operation of trains and…”
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Deep reinforcement learning based controller with dynamic feature extraction for an industrial claus process by Liu, Jialin, Tsai, Bing-Yen, Chen, Ding-Sou

“…•The DRL-based controller integrating with the process dynamics is developed.•The dynamic feature is extracted from the historical data using the Seq2seq…”
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Satellite attitude tracking control of moving targets combining deep reinforcement learning and predefined-time stability considering energy optimization by Shi, Zhong, Zhao, Fanyu, Wang, Xin, Jin, Zhonghe

“…•An attitude tracking control method combining deep reinforcement learning and traditional satellite attitude controller for moving targets tracking and…”
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Distributed deep reinforcement learning-based gas supply system coordination management method for solid oxide fuel cell by Li, Jiawen, Cui, Haoyang, Jiang, Wei

“…In order to sustain solid oxide fuel cell (SOFC) net output power and prevent violation of oxygen excess ratio (OER) constraint and fuel utilization (FU)…”
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Grid-area coordinated load frequency control strategy using large-scale multi-agent deep reinforcement learning by Li, Jiawen, Geng, Jian, Yu, Tao

“…In order to enable full participation of high-performance units controlled by different dispatching centers in the performance-based frequency regulation…”
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