We propose AttendLight, an end-to-end Reinforcement Learning (RL) algorithm for the
problem of traffic signal control. Previous approaches for this problem have the shortcoming …

Traditional control methods may not be adaptive enough for ever-changing traffic dynamics.
Hence, extensive deep reinforcement learning (DRL) methods have been utilized to solve …

We propose a toolkit for developing and comparing reinforcement learning (RL)-based traffic
signal controllers. The toolkit includes implementation of state-of-the-art deep-RL algorithms …

Traffic signal control is an important and challenging real-world problem, which aims to
minimize the travel time of vehicles by coordinating their movements at the road …

Ineffective and inflexible traffic signal control at urban intersections can often lead to
bottlenecks in traffic flows and cause congestion, delay, and environmental problems. How …

Deep reinforcement learning (RL) has been applied to traffic signal control recently and
demonstrated superior performance to conventional control methods. However, there are …

An efficient transportation system can substantially benefit our society, but road intersections
have always been among the major traffic bottlenecks leading to traffic congestion …

Traffic signal control is an important and challenging real-world problem that has recently
received a large amount of interest from both transportation and computer science …

With the increasing availability of traffic data and advance of deep reinforcement learning
techniques, there is an emerging trend of employing reinforcement learning (RL) for traffic …

Traffic signal control (TSC) is still one of the most significant and challenging research
problems in the transportation field. Reinforcement learning (RL) has achieved great …