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Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 919-931
作者:  Fei Ming;  Wenyin Gong;  Ling Wang;  Yaochu Jin
Adobe PDF(2091Kb)  |  收藏  |  浏览/下载:15/4  |  提交时间:2024/03/18
Constrained multi-objective optimization  deep Q-learning  deep reinforcement learning (DRL)  evolutionary algorithms  evolutionary operator selection  
Network-Wide Traffic Signal Control Based on MARL With Hierarchical Nash-Stackelberg Game Model 期刊论文
IEEE ACCESS, 2023, 卷号: 11, 页码: 145085-145100
作者:  Shen, Hui;  Zhao, Hongxia;  Zhang, Zundong;  Yang, Xun;  Song, Yutong;  Liu, Xiaoming
收藏  |  浏览/下载:9/0  |  提交时间:2024/02/22
Games  Roads  Approximation algorithms  Q-learning  Multi-agent systems  Process control  Optimization  Reinforcement learning  Traffic control  Network-wide traffic signal control  hierarchical game model  multi-agent reinforcement learning  
Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model 期刊论文
SENSORS, 2023, 卷号: 23, 期号: 24, 页码: 12
作者:  Yang, Yuanbo;  Li, Feimo;  Chang, Hongxing
收藏  |  浏览/下载:12/0  |  提交时间:2024/02/22
short track speed skating  deep reinforcement learning  decision-making method  deep Q-network  competition performance improvement  
Event-Triggered Deep Reinforcement Learning Using Parallel Control: A Case Study in Autonomous Driving 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 卷号: 8, 期号: 4, 页码: 2821-2831
作者:  Lu, Jingwei;  Han, Liyuan;  Wei, Qinglai;  Wang, Xiao;  Dai, Xingyuan;  Wang, Fei-Yue
收藏  |  浏览/下载:56/0  |  提交时间:2023/11/17
Autonomous vehicles  Decision making  Path planning  Training  Optimal control  Deep learning  Complex systems  Autonomous driving  deep reinforcement learning  deep Q-network  event-triggered control  parallel control  
Development of a Bias Compensating Q-Learning Controller for a Multi-Zone HVAC Facility 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 8, 页码: 1704-1715
作者:  Syed Ali Asad Rizvi;  Amanda J. Pertzborn;  Zongli Lin
Adobe PDF(4532Kb)  |  收藏  |  浏览/下载:115/55  |  提交时间:2023/07/20
HVAC control  optimal tracking  Q-learning  reinforcement learning (RL)  
Lane change decision-making through deep reinforcement learning with rule-based constraints 会议论文
, Budapest, Hungary, 2019-7
作者:  Wang JJ(王俊杰);  Zhang QC(张启超);  Zhao DB(赵冬斌);  Chen YR(陈亚冉)
Adobe PDF(295Kb)  |  收藏  |  浏览/下载:80/29  |  提交时间:2023/05/30
Lane Change  Decision-making  Deep Reinforcement Learning  Deep Q-Network  
Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 11, 页码: 2005-2019
作者:  Lijun Yue;  Houming Fan
Adobe PDF(6990Kb)  |  收藏  |  浏览/下载:142/44  |  提交时间:2022/10/09
Automated container terminal  dynamic scheduling  path planning  Q-Learning algorithm  rule-based heuristic algorithm  
VGN: Value Decomposition With Graph Attention Networks for Multiagent Reinforcement Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 14
作者:  Wei, Qinglai;  Li, Yugu;  Zhang, Jie;  Wang, Fei-Yue
收藏  |  浏览/下载:192/0  |  提交时间:2022/07/25
Mathematical models  Task analysis  Games  Q-learning  Neural networks  Behavioral sciences  Training  Deep learning  graph attention networks (GATs)  multiagent systems  reinforcement learning  
Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 3, 页码: 1228-1241
作者:  Zhu, Yuanheng;  Zhao, Dongbin
收藏  |  浏览/下载:179/0  |  提交时间:2022/06/10
Games  Nash equilibrium  Mathematical model  Markov processes  Convergence  Dynamic programming  Training  Deep reinforcement learning (DRL)  generalized policy iteration (GPI)  Markov game (MG)  Nash equilibrium  Q network  zero sum  
Picking out the Impurities: Attention-based Push-Grasping in Dense Clutter 期刊论文
ROBOTICA, 2022, 页码: 16
作者:  Lu, Ning;  Cai, Yinghao;  Lu, Tao;  Cao, Xiaoge;  Guo, Weiyan;  Wang, Shuo
收藏  |  浏览/下载:170/0  |  提交时间:2022/06/10
saliency Detection  robot Learning  push-grasp  target-oriented Grasping  deep Q Network