다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기
IEEE transactions on industrial informatics, v.20 no.2, 2024년, pp.2762 - 2775
Dai, Pengcheng (Jiangsu Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing, Jiangsu, China) , Yu, Wenwu (Frontiers Science Center for Mobile Information Communication and Security, School of Mathematics, Southeast University, Nanjing, China) , Wang, He (Jiangsu Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing, Jiangsu, China) , Jiang, Jiahui (School of Cyber Science and Engineering, Southeast University, Nanjing, China)
초록이 없습니다.
Shengdong, Mu, Zhengxian, Xiong, Yixiang, Tian. Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining. IEEE transactions on industrial informatics, vol.15, no.12, 6583-6592.
Jiang, Yongji, Feng, Wei-Jie, Wang, Lei, Kong, Xiangjie, Wang, Qing-Guo. A Rolling Optimization Algorithm for Real-Time Traffic Control With Delay Minimization. IEEE transactions on industrial informatics, vol.18, no.9, 5915-5924.
Haydari, Ammar, Yılmaz, Yasin. Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.23, no.1, 11-32.
Veres, Matthew, Moussa, Medhat. Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.21, no.8, 3152-3168.
Traffic Eng. Control The SCOOT on-line traffic signal optimisation technique Hunt 23 4 190 1982
Traffic Eng. Control Two traffic-responsive area traffic control methods: SCAT and SCOOT Luk 25 1 14 1984
Garcia-Nieto, J., Alba, E., Carolina Olivera, A.. Swarm intelligence for traffic light scheduling: Application to real urban areas. Engineering applications of artificial intelligence, vol.25, no.2, 274-283.
Ceylan, Halim, Bell, Michael G.H. Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing. Transportation research Part B, Methodological, vol.38, no.4, 329-342.
Gokulan, Balaji Parasumanna, Srinivasan, Dipti. Distributed Geometric Fuzzy Multiagent Urban Traffic Signal Control. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.11, no.3, 714-727.
Sutton, R.S., Barto, A.G.. Reinforcement Learning: An Introduction. IEEE transactions on neural networks, vol.9, no.5, 1054-1054.
Li, Fangyuan, Qin, Jiahu, Zheng, Wei Xing.
Distributed
Dai, Pengcheng, Yu, Wenwu, Wen, Guanghui, Baldi, Simone. Distributed Reinforcement Learning Algorithm for Dynamic Economic Dispatch With Unknown Generation Cost Functions. IEEE transactions on industrial informatics, vol.16, no.4, 2258-2267.
Dai, Pengcheng, Yu, Wenwu, Chen, Duxin. Distributed Q-Learning Algorithm for Dynamic Resource Allocation With Unknown Objective Functions and Application to Microgrid. IEEE transactions on cybernetics, vol.52, no.11, 12340-12350.
Yuan, Huanhuan, Xia, Yuanqing. Resilient strategy design for cyber-physical system under DoS attack over a multi-channel framework. Information sciences, vol.454, 312-327.
Ding, Kemi, Li, Yuzhe, Quevedo, Daniel E., Dey, Subhrakanti, Shi, Ling. A multi-channel transmission schedule for remote state estimation under DoS attacks. Automatica : the journal of IFAC, the International Federation of Automatic Control, vol.78, 194-201.
Dai, Pengcheng, Yu, Wenwu, Wang, He, Wen, Guanghui, Lv, Yuezu. Distributed Reinforcement Learning for Cyber-Physical System With Multiple Remote State Estimation Under DoS Attacker. IEEE transactions on network science and engineering, vol.7, no.4, 3212-3222.
Bazzan, Ana L. C.. Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Autonomous agents and multi-agent systems, vol.18, no.3, 342-375.
10.1145/3219819.3220096
Arel, I., Liu, C., Urbanik, T., Kohls, A.G.. Reinforcement learning-based multi-agent system for network traffic signal control. IET intelligent transport systems, vol.4, no.2, 128-135.
Wang, Xiaoqiang, Ke, Liangjun, Qiao, Zhimin, Chai, Xinghua. Large-Scale Traffic Signal Control Using a Novel Multiagent Reinforcement Learning. IEEE transactions on cybernetics, vol.51, no.1, 174-187.
10.1016/B978-1-55860-307-3.50049-6
10.1109/ITSC.2011.6083114
Proc. Learn. Inference Control Multi-Agent Syst. Coordinated deep reinforcement learners for traffic light control Van der Pol 1 2016
10.1145/3357384.3357900
Chu, Tianshu, Wang, Jie, Codecà, Lara, Li, Zhaojian. Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.21, no.3, 1086-1095.
10.1145/3357384.3357902
Chen, Chacha, Wei, Hua, Xu, Nan, Zheng, Guanjie, Yang, Ming, Xiong, Yuanhao, Xu, Kai, Li, Zhenhui. Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control. Proceedings of the ... aaai conference on artificial intelligence, vol.34, no.4, 3414-3421.
Foerster, Jakob, Farquhar, Gregory, Afouras, Triantafyllos, Nardelli, Nantas, Whiteson, Shimon. Counterfactual Multi-Agent Policy Gradients. Proceedings of the ... aaai conference on artificial intelligence, vol.32, no.1,
Proc. Int. Conf. Learn. Represent. Off-policy multi-agent decomposed policy gradients Wang 1 2021
Proc. 38th Int. Conf. Mach. Learn. A policy gradient algorithm for learning to learn in multiagent reinforcement learning Kim 5541 2021
Scalable centralized deep multi-agent reinforcement learning via policy gradients Khan 2018
Proc. Adv. Neural Inf. Process. Syst. Facmac: Factored multi-agent centralised policy gradients Peng 12208 2021
Proc. 38th Int. Conf. Mach. Learn. FOP: Factorizing optimal joint policy of maximum-entropy multi-agent reinforcement learning Zhang 12491 2021
Proc. Int. Conf. Mach. Learn. Finite-time analysis of distributed TD(0) with linear function approximation on multi-agent reinforcement learning Doan 1626 2019
Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics Krau 1998
10.1007/978-3-642-32460-4
10.1007/978-3-319-15024-6_7
Nedic, A., Ozdaglar, A.. Distributed Subgradient Methods for Multi-Agent Optimization. IEEE transactions on automatic control, vol.54, no.1, 48-61.
Proc. Int. Conf. Learn. Represent. Multi-agent reinforcement learning for networked system control Chu 1 2019
Proc. Adv. Neural Inf. Process. Syst. Learning multiagent communication with backpropagation Sukhbaatar 2244 2016
Proc. Adv. Neural Inf. Process. Syst. Learning to communicate with deep multi-agent reinforcement learning Foerster 2137 2016
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Copyright KISTI. All Rights Reserved.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.