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Researched and compared DQN, Double DQN, and Dueling Double DQN for autonomous vehicle lane changing in Carla simulation. Evaluated performance and collision rates to guide model selection for safer real-world applications. Findings contribute to enhancing autonomous vehicle safety and efficiency.
Implementations of algorithms from the Q-learning family. Implementations inlcude: DQN, DDQN, Dueling DQN, PER+DQN, Noisy DQN, C51
Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch
A minimalist environment for decision-making in autonomous driving
车辆的横向控制
纵向控制:PID ;横向控制:Stanely \ LQR \ MPC
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Data-Driven Documents codes.
China tencent open source team.