  In this paper, methods have been explored to effectively optimise traffic
signal control to minimise waiting times and queue lengths, thereby increasing
traffic flow. The traffic intersection was first defined as a Markov Decision
Process, and a state representation, actions and rewards were chosen.
Simulation of Urban MObility (SUMO) was used to simulate an intersection and
then compare a Round Robin Scheduler, a Feedback Control mechanism and two
Reinforcement Learning techniques - Deep Q Network (DQN) and Advantage
Actor-Critic (A2C), as the policy for the traffic signal in the simulation
under different scenarios. Finally, the methods were tested on a simulation of
a real-world intersection in Bengaluru, India.

Systems and Control;Machine Learning;Systems and Control