FP-WDDQN: An improved deep reinforcement learning algorithm for adaptive traffic signal control

Current adaptive traffic signal control methods based on centralized deep reinforcement learning are not applicable in large-scale adaptive traffic control environment. The scalability problem is overcome by assigning global control to each local RL agent through multi-intelligence reinforcement lea...

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Veröffentlicht in:	IEEE ... International Conference on Data Mining workshops S. 44 - 51
Hauptverfasser:	Zhang, Xiao, Xu, Xiaolong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 04.12.2023
Schlagworte:	Deep learning Deep reinforcement learning Finite element analysis Forgetful Experience Mechanism Heuristic algorithms Intelligent traffic light control Multi-agent technology Prioritized Experience Replay Scalability Traffic control Training Vehicle dynamics
ISSN:	2375-9259
Online-Zugang:	Volltext
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