FREEWAY TRAFFIC CONTROL USING Q-LEARNING

In this paper, the standard Q-learning algorithm is applied to control the density of an arbitrary freeway via ramp metering in a macroscopic level. The reinforcement learning algorithms have proven to be effective tools for letting an agent learn from its experiences generated by its interaction with an environment. The performance of the algorithm as well as its robustness against communication failure is studied. The results of the simulations demonstrated the effectiveness the technique.