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Deep Reinforcement Learning for Traffic Control by chanwengqiu is a document available to read on EtoBox.

What is Deep Reinforcement Learning for Traffic Control about?

This paper proposes a deep reinforcement learning algorithm for adaptive traffic signal control. The algorithm uses a convolutional neural network to extract useful features from raw traffic data and learn the optimal traffic signal control policy. Experience replay and target networks are used to improve algorithm stability.

Author

chanwengqiu

Language

EN