Improvement of traffic signal control (TSC) efficiency has been found to lead to improved
urban transportation and enhanced quality of life. Recently, the use of reinforcement …

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 …

Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …

Big data is becoming a research focus in intelligent transportation systems (ITS), which can
be seen in many projects around the world. Intelligent transportation systems will produce a …

Exploration is critical for good results in deep reinforcement learning and has attracted much
attention. However, existing multi-agent deep reinforcement learning algorithms still use …

Many real-world applications can be described as large-scale games of imperfect
information. To deal with these challenging domains, prior work has focused on computing …

Background The intersection traffic signal control problem (ITSCP) has become even more
important as traffic congestion has been more intractable. The ITSCP seeks an efficient …

Offline reinforcement learning leverages previously collected offline datasets to learn
optimal policies with no necessity to access the real environment. Such a paradigm is also …

Intelligent transport systems, efficient electric grids, and sensor networks for data collection
and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve …

The transportation demand is rapidly growing in metropolises, resulting in chronic traffic
congestions in dense downtown areas. Adaptive traffic signal control as the principle part of …