NUST Institutions Library Catalogue

This thesis presents an in-depth examination of traffic light control optimization using
Reinforcement Learning (RL) techniques. The research focuses on two specific RL algorithms: Deep Q-Learning (DQN) and Double Deep Q-Learning (DDQN), investigating
their ability to reduce wait times at a four-way traffic intersection. The RL agents’
learning process is driven by a reward function based on waiting times, designed to
guide the agents towards minimizing these times. A transition phase is implemented
in the model, allowing for flexibility and responsiveness to changing traffic conditions.
Deep Neural Networks (DNNs) are used as function approximators, facilitating the understanding of the association between state-action pairs. The architecture comprises
five fully connected hidden layers, providing an effective means of approximating the Qvalues for the state-action pairs. Training data for the DNN is stored in an Experience
Replay Memory, which is effectively a history of state, action, reward, and subsequent
state. The study concludes that both DQN and DDQN agents demonstrated an increasing proficiency over time, indicating the successful application of RL techniques
in traffic light control systems. This research contributes to the ongoing efforts to employ advanced RL techniques in optimizing traffic flow, with potential applications in
intelligent transportation systems, smart cities, and autonomous vehicle navigation.