izvorni znanstveni rad
Variable Speed Limit Control (VSLC) is one control method for alleviating congestions on urban motorways. Machine learning techniques, like Reinforcement Learning (RL), are a promising alternative for setting up VSLC because an optimal control policy can be achieved with a smaller computational burden in comparison with optimal control approaches. A drawback is a large number of learning iterations and the problem of the exponential expansion of the state space dimension. This can be solved with function approximation techniques. Three different approaches for feature-based state representation in RL based VSLC are compared in this paper regarding the convergence of Total Time Spent. The microscopic traffic simulator VISSIM with a representative traffic model is used to evaluate the compared approaches. Results show that function approximation methods outperform RL based VSLC formulated with a lookup table by an average improvement of 10 %, where feature extraction methods (Coarse and Tile) coding showed slightly faster learning rate.
Intelligent transportation systems, Intelligent control systems, Variable speed limit control, Reinforcement learning, Tile coding, Coarse coding, RBF
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