  Nowadays, transportation networks face the challenge of sub-optimal control
policies that can have adverse effects on human health, the environment, and
contribute to traffic congestion. Increased levels of air pollution and
extended commute times caused by traffic bottlenecks make intersection traffic
signal controllers a crucial component of modern transportation infrastructure.
Despite several adaptive traffic signal controllers in literature, limited
research has been conducted on their comparative performance. Furthermore,
despite carbon dioxide (CO2) emissions' significance as a global issue, the
literature has paid limited attention to this area. In this report, we propose
EcoLight, a reward shaping scheme for reinforcement learning algorithms that
not only reduces CO2 emissions but also achieves competitive results in metrics
such as travel time. We compare the performance of tabular Q-Learning, DQN,
SARSA, and A2C algorithms using metrics such as travel time, CO2 emissions,
waiting time, and stopped time. Our evaluation considers multiple scenarios
that encompass a range of road users (trucks, buses, cars) with varying
pollution levels.
