Adaptive Broad Deep Reinforcement Learning for Intelligent Traffic Light Control

Deep reinforcement learning (DRL) has superior autonomous decision-making capabilities, combining deep learning and reinforcement learning (RL). Unlike DRL employs deep neural networks (DNNs), broad RL (BRL) adopts the broad learning system (BLS) that is established with flat networks to generate th...

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Veröffentlicht in:	IEEE internet of things journal 2024-09, Vol.11 (17), p.28496-28507
Hauptverfasser:	Zhu, Ruijie, Wu, Shuning, Li, Lulu, Ding, Wenting, Lv, Ping, Sui, Luyao
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive control Algorithms Artificial neural networks Autonomous vehicles Broad learning system (BLS) broad reinforcement learning (BRL) Deep learning Deep reinforcement learning deep reinforcement learning (DRL) Feature extraction Finite element analysis Internet of Things Machine learning multiagent DRL (MADRL) Multiagent systems Optimization Random sampling Reagents Robustness Traffic control Traffic information traffic light control (TLC) Traffic signals Training
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