Autonomous Driving in Reality with Reinforcement Learning and Image Translation [article]

Nayun Xu, Bowen Tan, Bingyu Kong
2019 arXiv   pre-print
Supervised learning is widely used in training autonomous driving vehicle. However, it is trained with large amount of supervised labeled data. Reinforcement learning can be trained without abundant labeled data, but we cannot train it in reality because it would involve many unpredictable accidents. Nevertheless, training an agent with good performance in virtual environment is relatively much easier. Because of the huge difference between virtual and real, how to fill the gap between virtual
more » ... nd real is challenging. In this paper, we proposed a novel framework of reinforcement learning with image semantic segmentation network to make the whole model adaptable to reality. The agent is trained in TORCS, a car racing simulator.
arXiv:1801.05299v2 fatcat:rsf4qxdb3ffehj7kprt2eybjhm