End to end autonomous driving simulation based on reinforcement learning - Wong, Kenzhi Iskandar (Nanyang Technological University, 2024)
This paper presents a comprehensive study that explores the application of reinforcement learning (RL) algorithms, specifically Deep Q-Network (DQN) and Soft Actor Critic (SAC), in the context of end-to-end autonomous driving. The research project utilizes the SMARTS Simulator, an open-source software tailored for RL applications in autonomous driving scenarios. Employing an end-to-end approach, the research project utilizes RGB image inputs into an Artificial Neural Network, with image recognition facilitated by the Vision Transformer model.