Rapid and continuous advances in communications and computer technology are spurring
a host of new concepts in road traffic control. From simple traffic control measures like
lane segregation for wheeled and pedestrian traffic in the 14th century to the use of artificial
intelligence to control traffic, we have come a long way. The advancement of technology in
the automotive sector along with the industrial revolution led to the number of vehicle owners
seeing a drastic increase in the 20th century. There was a growing need for infrastructure to
handle this new volume of vehicles which led to the construction of several road networks in
both urban and freeway settings. Adding infrastructure was a valid method to handle traffic
problems until we realized that each new addition required a significant amount of land that
was slowly reducing. This brought along the need for newer and more efficient traffic control
techniques due to increased vehicles on the road, the different kinds of vehicles itself and the
need to reduce construction of roads as a means of reducing traffic.
In this thesis report, we discuss the different control methods using reinforcement learning
to tackle the freeway traffic control problem. The thesis covers the fundamentals of freeway
traffic control, reinforcement learning and the agents used for control. It focuses on the
creation of the freeway network, environment setup for reinforcement learning application
and the choice of agents mainly SAC in order to implement continuous actions to improve
combined ramp metering and variable speed limit control in more complex scenarios