  Variable speed limits (VSL) control is a flexible way to improve traffic
condition,increase safety and reduce emission. There is an emerging trend of
using reinforcement learning technique for VSL control and recent studies have
shown promising results. Currently, deep learning is enabling reinforcement
learning to develope autonomous control agents for problems that were
previously intractable. In this paper, we propose a more effective deep
reinforcement learning (DRL) model for differential variable speed limits
(DVSL) control, in which the dynamic and different speed limits among lanes can
be imposed. The proposed DRL models use a novel actor-critic architecture which
can learn a large number of discrete speed limits in a continues action space.
Different reward signals, e.g. total travel time, bottleneck speed, emergency
braking, and vehicular emission are used to train the DVSL controller, and
comparison between these reward signals are conducted. We test proposed DRL
baased DVSL controllers on a simulated freeway recurrent bottleneck. Results
show that the efficiency, safety and emissions can be improved by the proposed
method. We also show some interesting findings through the visulization of the
control policies generated from DRL models.
