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Urban Traffic Control Using Adjusted Reinforcement Learning in a Multi-agent System
2013
Research Journal of Applied Sciences Engineering and Technology
Dynamism, continuous changes of states and the necessity to respond quickly are the specific characteristics of the environment in a traffic control system. Proposing an appropriate and flexible strategy to meet the existing requirements is always an important issue in traffic control. This study presents an adaptive approach to control urban traffic using multi-agent systems and a reinforcement learning augmented by an adjusting pre-learning stage. In this approach, the agent primarily uses
doi:10.19026/rjaset.6.3676
fatcat:gdl275musbdqlfnchqzvzpas5y