Deep reinforcement learning for traffic light control in vehicular networks

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Coordinated deep reinforcement learners for traffic light control E van der Pol, FA Oliehoek NIPS WS: Learning, Inference and Control of Multi-Agent Systems , 2016

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Jul 25, 2019 · Traffic signal timing via deep reinforcement learning. IEEE/CAA Journal of Automatica Sinica , Vol. 3, 3 (2016), 247--254. Google Scholar Cross Ref; Xiaoyuan Liang, Xunsheng Du, Guiling Wang, and Zhu Han. 2018. Deep reinforcement learning for traffic light control in vehicular networks. arXiv preprint arXiv:1803.11115 (2018). Google Scholar

Feudal Multi-Agent Deep Reinforcement Learning for Traffic Signal Control∗ Jinming Ma School of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, China [email protected] Feng Wu† School of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, China

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Coordinated deep reinforcement learners for traffic light control E van der Pol, FA Oliehoek NIPS WS: Learning, Inference and Control of Multi-Agent Systems , 2016

Request PDF | Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks | Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy.

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Deep Deterministic Policy Gradients deep reinforcement learning traffic flow cloud computation Deep Q-Networks More (9+) Weibo : It would be interesting to investigate the effectiveness of other Reinforcement Learning methods, like Deep Deterministic Policy Gradients used in, for transportation systems

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Nov 13, 2018 · Traffic light technology is antiquated; it is typically based on fixed time intervals that become ineffective during peak hours or special events (i.e. sporting events or concerts). Machine learning can be implemented to manage roadways and optimize their efficiency in real time and alleviate some of these issues.

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An efficient traffic signal control system (TSCS) should not only be reactive to the current traffic but also be predictive by anticipating future traffic disturbances. In this study, we investigate the potential of using convolution neural network (CNN) in detecting emergency cases and forecasting events that can interrupt the traffic flow. Case-based reasoning (CBR) is then exploited to ...

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Request PDF | Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks | Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy.

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Nov 13, 2018 · Traffic light technology is antiquated; it is typically based on fixed time intervals that become ineffective during peak hours or special events (i.e. sporting events or concerts). Machine learning can be implemented to manage roadways and optimize their efficiency in real time and alleviate some of these issues.
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Jan 01, 2019 · Deep Q-Learning paper explained: Human-level control through deep reinforcement learning (algorithm) - Duration: 1:24:30. ML Explained - A.I. Socratic Circles - AISC 1,494 views 1:24:30
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Coordinated deep reinforcement learners for traffic light control E van der Pol, FA Oliehoek NIPS WS: Learning, Inference and Control of Multi-Agent Systems , 2016
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