Deep Q learning for traffic simulation in autonomous driving at a highway junction

Trafïîc congestion is a serious global problem. Enhanced or deep Q learning algorithms were applied to a traffic simulation study. The enhanced Q learning was based on a repeated local search algorithm, and it was able to find optimal pathways under a multi-agent system. Deep Q learning was also capable of learning a suitable strategy, considering dynamic changes in traffic circumstances at a highway junction. In particular, the target network of the Q learning realized a stable regulation of the loss function. This intelligent method based on deep reinforcement learning could become an effective tool to optimize car pathways including an autonomous driving system.