  Machine learning techniques have outperformed numerous rule-based methods for
decision-making in autonomous vehicles. Despite recent efforts, lane changing
remains a major challenge, due to the complex driving scenarios and changeable
social behaviors of surrounding vehicles. To help improve the state of the art,
we propose to leveraging the emerging \underline{D}eep
\underline{R}einforcement learning (DRL) approach for la\underline{NE} changing
at the \underline{T}actical level. To this end, we present "DRNet", a novel and
highly efficient DRL-based framework that enables a DRL agent to learn to drive
by executing reasonable lane changing on simulated highways with an arbitrary
number of lanes, and considering driving style of surrounding vehicles to make
better decisions. Furthermore, to achieve a safe policy for decision-making,
DRNet incorporates ideas from safety verification, the most important component
of autonomous driving, to ensure that only safe actions are chosen at any time.
The setting of our state representation and reward function enables the trained
agent to take appropriate actions in a real-world-like simulator. Our DRL agent
has the ability to learn the desired task without causing collisions and
outperforms DDQN and other baseline models.
