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cnn-keras

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albinjm / DeepTrafficQ

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DeepTrafficQ is a reinforcement learning-based traffic signal control system that uses Deep Q-Networks (DQN) to minimize vehicle waiting times at a 4-way intersection. By leveraging Q-learning with experience replay and a convolutional neural network (CNN), the agent dynamically adjusts traffic light phases to optimize traffic flow.

reinforcement-learning q-learning cnn sumo convolutional-neural-networks deep-q-network cnn-keras smart-cities smart-mobility traffic-optimization experience-replay adaptive-traffic-light-control traffic-signal-control intelligent-transportation q-learning-algorithm iot-traffic-surveillance dqn-agent intelligent-transportation-system
  • Updated Jun 29, 2025
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